x

.github/CONTRIBUTING.md

@@ -3,4 +3,43 @@
 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 they involve new features, improved infrastructure, better documentation, or bug fixes.
 
-To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/).
+To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
+
+## 🗺️ Guidelines
+
+### 👩‍💻 Ways to contribute
+
+There are many ways to contribute to LangChain. Here are some common ways people contribute:
+
+- [**Documentation**](https://python.langchain.com/docs/contributing/documentation): Help improve our docs, including this one!
+- [**Code**](https://python.langchain.com/docs/contributing/code): Help us write code, fix bugs, or improve our infrastructure.
+- [**Integrations**](https://python.langchain.com/docs/contributing/integration): Help us integrate with your favorite vendors and tools.
+
+### 🚩GitHub Issues
+
+Our [issues](https://github.com/langchain-ai/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. 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 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+with the rapid rate of development in this field some may get out of date.
+If you notice this happening, please let us know.
+
+### 🙋Getting Help
+
+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.
+
+### Contributor Documentation
+
+To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)

.github/DISCUSSION_TEMPLATE/q-a.yml

@@ -1,122 +0,0 @@
-labels: [Question]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for your interest in 🦜️🔗 LangChain!
-
-        Please follow these instructions, fill every question, and do every step. 🙏
-        
-        We're asking for this because answering questions and solving problems in GitHub takes a lot of time --
-        this is time that we cannot spend on adding new features, fixing bugs, write documentation or reviewing pull requests.
-
-        By asking questions in a structured way (following this) it will be much easier to help you.
-
-        And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎
-
-        As there are too many questions, we will **DISCARD** and close the incomplete ones. 
-        
-        That will allow us (and others) to focus on helping people like you that follow the whole process. 🤓
-        
-        Relevant links to check before opening a question to see if your question has already been answered, fixed or
-        if there's another way to solve your problem:
-        
-        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [API Reference](https://api.python.langchain.com/en/stable/),
-        [GitHub search](https://github.com/langchain-ai/langchain),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
-        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
-        [LangChain ChatBot](https://chat.langchain.com/)
-  - type: checkboxes
-    id: checks
-    attributes:
-      label: Checked other resources
-      description: Please confirm and check all the following options.
-      options:
-        - label: I added a very descriptive title to this question.
-          required: true
-        - label: I searched the LangChain documentation with the integrated search.
-          required: true
-        - label: I used the GitHub search to find a similar question and didn't find it.
-          required: true
-  - type: checkboxes
-    id: help
-    attributes:
-      label: Commit to Help
-      description: |
-        After submitting this, I commit to one of:
-
-          * Read open questions until I find 2 where I can help someone and add a comment to help there.
-          * I already hit the "watch" button in this repository to receive notifications and I commit to help at least 2 people that ask questions in the future.
-          * Once my question is answered, I will mark the answer as "accepted".
-      options:
-        - label: I commit to help with one of those options 👆
-          required: true
-  - type: textarea
-    id: example
-    attributes:
-      label: Example Code
-      description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
-        
-        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
-        
-        **Important!** 
-        
-        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
-        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        from langchain_core.runnables import RunnableLambda
-
-        def bad_code(inputs) -> int:
-          raise NotImplementedError('For demo purpose')
-        
-          chain = RunnableLambda(bad_code)
-          chain.invoke('Hello!')
-      render: python
-    validations:
-      required: true
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: |
-        What is the problem, question, or error?
-
-        Write a short description explaining what you are doing, what you expect to happen, and what is currently happening.
-      placeholder: |
-        * I'm trying to use the `langchain` library to do X.
-        * I expect to see Y.
-        * Instead, it does Z.
-    validations:
-      required: true
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: |
-        Please share your system info with us. 
-        
-        "pip freeze | grep langchain" 
-        platform (windows / linux / mac)
-        python version
-        
-        OR if you're on a recent version of langchain-core you can paste the output of:
-        
-        python -m langchain_core.sys_info
-      placeholder: |
-        "pip freeze | grep langchain"
-        platform
-        python version
-        
-        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
-        
-        python -m langchain_core.sys_info
-        
-        These will only surface LangChain packages, don't forget to include any other relevant
-        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
+description: Submit a bug report to help us improve LangChain. To report a security issue, please instead use the security option below.
 labels: ["02 Bug Report"]
 body:
   - type: markdown
@@ -7,11 +7,6 @@ body:
       value: >
         Thank you for taking the time to file a bug report. 
         
-        Use this to report bugs in LangChain. 
-        
-        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
-        to ask for help with your issue.
-        
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
         if there's another way to solve your problem:
         
@@ -19,8 +14,7 @@ body:
         [API Reference](https://api.python.langchain.com/en/stable/),
         [GitHub search](https://github.com/langchain-ai/langchain),
         [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
-        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
-        [LangChain ChatBot](https://chat.langchain.com/)
+        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue)
   - type: checkboxes
     id: checks
     attributes:
@@ -33,8 +27,6 @@ body:
           required: true
         - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
-        - label: I am sure that this is a bug in LangChain rather than my code.
-          required: true
   - type: textarea
     id: reproduction
     validations:
@@ -46,12 +38,10 @@ body:
         
         If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
         
-        **Important!** 
+        If you're including an error message, please include the full stack trace not just the last error.
         
-        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
-        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        **Important!** Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
 
       placeholder: |
         The following code: 
@@ -65,16 +55,9 @@ body:
           chain = RunnableLambda(bad_code)
           chain.invoke('Hello!')
         ```
-  - type: textarea
-    id: error
-    validations:
-      required: false
-    attributes:
-      label: Error Message and Stack Trace (if applicable)
-      description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
+        
+        Include both the error and the full stack trace if reporting an exception!
+
   - type: textarea
     id: description
     attributes:
@@ -93,26 +76,28 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: |
-        Please share your system info with us. 
-        
-        "pip freeze | grep langchain" 
-        platform (windows / linux / mac)
-        python version
-        
-        OR if you're on a recent version of langchain-core you can paste the output of:
-        
-        python -m langchain_core.sys_info
+      description: Please share your system info with us.
       placeholder: |
         "pip freeze | grep langchain"
         platform
         python version
-        
-        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
-        
-        python -m langchain_core.sys_info
-        
-        These will only surface LangChain packages, don't forget to include any other relevant
-        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true
+  - type: checkboxes
+    id: related-components
+    attributes:
+      label: Related Components
+      description: "Select the components related to the issue (if applicable):"
+      options:
+        - label: "LLMs/Chat Models"
+        - label: "Embedding Models"
+        - label: "Prompts / Prompt Templates / Prompt Selectors"
+        - label: "Output Parsers"
+        - label: "Document Loaders"
+        - label: "Vector Stores / Retrievers"
+        - label: "Memory"
+        - label: "Agents / Agent Executors"
+        - label: "Tools / Toolkits"
+        - label: "Chains"
+        - label: "Callbacks/Tracing"
+        - label: "Async"

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +1,9 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
   - name: 🤔 Question or Problem
     about: Ask a question or ask about a problem in GitHub Discussions.
-    url: https://www.github.com/langchain-ai/langchain/discussions/categories/q-a
+    url: https://github.com/langchain-ai/langchain/discussions
   - name: Discord
     url: https://discord.gg/6adMQxSpJS
     about: General community discussions
-  - name: Feature Request
-    url: https://www.github.com/langchain-ai/langchain/discussions/categories/ideas
-    about: Suggest a feature or an idea
-  - name: Show and tell
-    about: Show what you built with LangChain
-    url: https://www.github.com/langchain-ai/langchain/discussions/categories/show-and-tell

.github/ISSUE_TEMPLATE/documentation.yml

@@ -4,45 +4,13 @@ title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
 labels: [03 - Documentation]
 
 body:
-- type: markdown
-  attributes:
-    value: >
-      Thank you for taking the time to report an issue in the documentation.
-      
-      Only report issues with documentation here, explain if there are
-      any missing topics or if you found a mistake in the documentation.
-      
-      Do **NOT** use this to ask usage questions or reporting issues with your code.
-      
-      If you have usage questions or need help solving some problem, 
-      please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions).
-      
-      If you're in the wrong place, here are some helpful links to find a better
-      place to ask your question:
-      
-      [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-      [API Reference](https://api.python.langchain.com/en/stable/),
-      [GitHub search](https://github.com/langchain-ai/langchain),
-      [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
-      [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
-      [LangChain ChatBot](https://chat.langchain.com/)
-- type: checkboxes
-  id: checks
-  attributes:
-    label: Checklist
-    description: Please confirm and check all the following options.
-    options:
-      - label: I added a very descriptive title to this issue.
-        required: true
-      - label: I included a link to the documentation page I am referring to (if applicable).
-        required: true
 - type: textarea
   attributes: 
     label: "Issue with current documentation:"
     description: >
       Please make sure to leave a reference to the document/code you're
-      referring to. Feel free to include names of classes, functions, methods
-      or concepts you'd like to see documented more.
+      referring to.
+
 - type: textarea
   attributes:
     label: "Idea or request for content:"

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,17 +1,7 @@
-labels: [idea]
+name: "\U0001F680 Feature request"
+description: Submit a proposal/request for a new LangChain feature
+labels: ["02 Feature Request"]
 body:
-  - type: checkboxes
-    id: checks
-    attributes:
-      label: Checked
-      description: Please confirm and check all the following options.
-      options:
-        - label: I searched existing ideas and did not find a similar one
-          required: true
-        - label: I added a very descriptive title
-          required: true
-        - label: I've clearly described the feature request and motivation for it
-          required: true
   - type: textarea
     id: feature-request
     validations:
@@ -20,6 +10,7 @@ body:
       label: Feature request
       description: |
         A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
+
   - type: textarea
     id: motivation
     validations:
@@ -28,11 +19,12 @@ body:
       label: Motivation
       description: |
         Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
+
   - type: textarea
-    id: proposal
+    id: contribution
     validations:
-      required: false
+      required: true
     attributes:
-      label: Proposal (If applicable)
+      label: Your contribution
       description: |
-        If you would like to propose a solution, please describe it here. 
+        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the [Contributing Guide](https://python.langchain.com/docs/contributing/)

.github/ISSUE_TEMPLATE/privileged.yml

@@ -1,25 +0,0 @@
-name: 🔒 Privileged
-description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for your interest in LangChain! 🚀
-        
-        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
-        
-        You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
-        or are a regular contributor to LangChain with previous merged merged pull requests.
-  - type: checkboxes
-    id: privileged
-    attributes:
-      label: Privileged issue
-      description: Confirm that you are allowed to create an issue here.
-      options:
-        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
-          required: true
-  - type: textarea
-    id: content
-    attributes:
-      label: Issue Content
-      description: Add the content of the issue here.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,29 +1,20 @@
-Thank you for contributing to LangChain!
+<!-- Thank you for contributing to LangChain!
 
-- [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
-  - Example: "community: add foobar LLM"
+Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
 
+Replace this entire comment with:
+  - **Description:** a description of the change, 
+  - **Issue:** the issue # it fixes if applicable,
+  - **Dependencies:** any dependencies required for this change,
+  - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
 
-- [ ] **PR message**: ***Delete this entire checklist*** and replace with
-    - **Description:** a description of the change
-    - **Issue:** the issue # it fixes, if applicable
-    - **Dependencies:** any dependencies required for this change
-    - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
+Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
 
+See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
 
-- [ ] **Add tests and docs**: If you're adding a new integration, please include
+If you're adding a new integration, please include:
   1. a test for the integration, preferably unit tests that do not rely on network access,
   2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
 
-
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/
-
-Additional guidelines:
-- Make sure optional dependencies are imported within a function.
-- Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.
-- Most PRs should not touch more than one package.
-- Changes should be backwards compatible.
-- If you are adding something to community, do not re-import it in langchain.
-
-If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17.
+If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
+ -->

.github/actions/poetry_setup/action.yml

@@ -32,7 +32,7 @@ runs:
       with:
         python-version: ${{ inputs.python-version }}
 
-    - uses: actions/cache@v4
+    - uses: actions/cache@v3
       id: cache-bin-poetry
       name: Cache Poetry binary - Python ${{ inputs.python-version }}
       env:
@@ -79,7 +79,7 @@ runs:
       run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
 
     - name: Restore pip and poetry cached dependencies
-      uses: actions/cache@v4
+      uses: actions/cache@v3
       env:
         SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
         WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}

.github/scripts/check_diff.py

@@ -36,7 +36,13 @@ if __name__ == "__main__":
         elif "libs/partners" in file:
             partner_dir = file.split("/")[2]
             if os.path.isdir(f"libs/partners/{partner_dir}"):
-                dirs_to_run.add(f"libs/partners/{partner_dir}")
+                dirs_to_run.update(
+                    (
+                        f"libs/partners/{partner_dir}",
+                        "libs/langchain",
+                        "libs/experimental",
+                    )
+                )
             # Skip if the directory was deleted
         elif "libs/langchain" in file:
             dirs_to_run.update(("libs/langchain", "libs/experimental"))
@@ -47,4 +53,4 @@ if __name__ == "__main__":
         else:
             pass
     json_output = json.dumps(list(dirs_to_run))
-    print(f"dirs-to-run={json_output}")  # noqa: T201
+    print(f"dirs-to-run={json_output}")

.github/scripts/get_min_versions.py

@@ -1,67 +0,0 @@
-import sys
-
-import tomllib
-from packaging.version import parse as parse_version
-import re
-
-MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain"]
-
-
-def get_min_version(version: str) -> str:
-    # case ^x.x.x
-    _match = re.match(r"^\^(\d+(?:\.\d+){0,2})$", version)
-    if _match:
-        return _match.group(1)
-
-    # case >=x.x.x,<y.y.y
-    _match = re.match(r"^>=(\d+(?:\.\d+){0,2}),<(\d+(?:\.\d+){0,2})$", version)
-    if _match:
-        _min = _match.group(1)
-        _max = _match.group(2)
-        assert parse_version(_min) < parse_version(_max)
-        return _min
-
-    # case x.x.x
-    _match = re.match(r"^(\d+(?:\.\d+){0,2})$", version)
-    if _match:
-        return _match.group(1)
-
-    raise ValueError(f"Unrecognized version format: {version}")
-
-
-def get_min_version_from_toml(toml_path: str):
-    # Parse the TOML file
-    with open(toml_path, "rb") as file:
-        toml_data = tomllib.load(file)
-
-    # Get the dependencies from tool.poetry.dependencies
-    dependencies = toml_data["tool"]["poetry"]["dependencies"]
-
-    # Initialize a dictionary to store the minimum versions
-    min_versions = {}
-
-    # Iterate over the libs in MIN_VERSION_LIBS
-    for lib in MIN_VERSION_LIBS:
-        # Check if the lib is present in the dependencies
-        if lib in dependencies:
-            # Get the version string
-            version_string = dependencies[lib]
-
-            # Use parse_version to get the minimum supported version from version_string
-            min_version = get_min_version(version_string)
-
-            # Store the minimum version in the min_versions dictionary
-            min_versions[lib] = min_version
-
-    return min_versions
-
-
-# Get the TOML file path from the command line argument
-toml_file = sys.argv[1]
-
-# Call the function to get the minimum versions
-min_versions = get_min_version_from_toml(toml_file)
-
-print(
-    " ".join([f"{lib}=={version}" for lib, version in min_versions.items()])
-)  # noqa: T201

.github/workflows/_all_ci.yml

@@ -36,35 +36,30 @@ env:
 
 jobs:
   lint:
-    name: "-"
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   test:
-    name: "-"
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   compile-integration-tests:
-    name: "-"
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   dependencies:
-    name: "-"
     uses: ./.github/workflows/_dependencies.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   extended-tests:
-    name: "make extended_tests #${{ matrix.python-version }}"
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -73,6 +68,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+    name: Python ${{ matrix.python-version }} extended tests
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_compile_integration_test.yml

@@ -24,7 +24,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-    name: "poetry run pytest -m compile tests/integration_tests #${{ matrix.python-version }}"
+    name: Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 

.github/workflows/_dependencies.yml

@@ -28,7 +28,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-    name: dependency checks ${{ matrix.python-version }}
+    name: dependencies - Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 

.github/workflows/_integration_test.yml

@@ -12,7 +12,6 @@ env:
 
 jobs:
   build:
-    environment: Scheduled testing
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}
@@ -38,11 +37,6 @@ jobs:
         shell: bash
         run: poetry install --with test,test_integration
 
-      - name: Install deps outside pyproject
-        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
-        shell: bash
-        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
-
       - name: 'Authenticate to Google Cloud'
         id: 'auth'
         uses: google-github-actions/auth@v2
@@ -52,24 +46,11 @@ jobs:
       - name: Run integration tests
         shell: bash
         env:
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
-          GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
-          GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
-          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
-          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: |
           make integration_tests
 

.github/workflows/_lint.yml

@@ -21,7 +21,6 @@ env:
 
 jobs:
   build:
-    name: "make lint #${{ matrix.python-version }}"
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -80,13 +79,13 @@ jobs:
           poetry run pip install -e "$LANGCHAIN_LOCATION"
 
       - name: Get .mypy_cache to speed up mypy
-        uses: actions/cache@v4
+        uses: actions/cache@v3
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
 
 
       - name: Analysing the code with our lint
@@ -94,7 +93,7 @@ jobs:
         run: |
           make lint_package
 
-      - name: Install unit test dependencies
+      - name: Install test dependencies
         # Also installs dev/lint/test/typing dependencies, to ensure we have
         # type hints for as many of our libraries as possible.
         # This helps catch errors that require dependencies to be spotted, for example:
@@ -103,24 +102,18 @@ jobs:
         # If you change this configuration, make sure to change the `cache-key`
         # in the `poetry_setup` action above to stop using the old cache.
         # It doesn't matter how you change it, any change will cause a cache-bust.
-        if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |
           poetry install --with test
-      - name: Install unit+integration test dependencies
-        if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry install --with test,test_integration
 
       - name: Get .mypy_cache_test to speed up mypy
-        uses: actions/cache@v4
+        uses: actions/cache@v3
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_release.yml

@@ -15,13 +15,12 @@ on:
         default: 'libs/langchain'
 
 env:
-  PYTHON_VERSION: "3.11"
+  PYTHON_VERSION: "3.10"
   POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
     if: github.ref == 'refs/heads/master'
-    environment: Scheduled testing
     runs-on: ubuntu-latest
 
     outputs:
@@ -166,48 +165,18 @@ jobs:
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-          NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
-          GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
-          GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
-          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
-          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
-          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
-      - name: Get minimum versions
-        working-directory: ${{ inputs.working-directory }}
-        id: min-version
-        run: |
-          poetry run pip install packaging
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)"
-          echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
-          echo "min-versions=$min_versions"
-
       - name: Run unit tests with minimum dependency versions
-        if: ${{ steps.min-version.outputs.min-versions != '' }}
-        env:
-          MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
+        if: ${{ (inputs.working-directory == 'libs/langchain') || (inputs.working-directory == 'libs/community') || (inputs.working-directory == 'libs/experimental') }}
         run: |
-          poetry run pip install $MIN_VERSIONS
+          poetry run pip install -r _test_minimum_requirements.txt
           make tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/_test.yml

@@ -28,7 +28,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-    name: "make test #${{ matrix.python-version }}"
+    name: Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 

.github/workflows/api_doc_build.yml

@@ -1,52 +0,0 @@
-name: API docs build
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron:  '0 13 * * *'
-env:
-  POETRY_VERSION: "1.7.1"
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: bagatur/api_docs_build
-
-      - name: Set Git config
-        run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "Github Actions"
-
-      - name: Merge master
-        run: | 
-          git fetch origin master
-          git merge origin/master -m "Merge master" --allow-unrelated-histories -X theirs
-
-      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: api-docs
-
-      - name: Install dependencies
-        run: |
-          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
-          poetry run python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
-          poetry run python -m pip install ./libs/partners/*
-          poetry run python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
-
-      - name: Build docs
-        run: |
-          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
-          poetry run python docs/api_reference/create_api_rst.py
-          poetry run python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference api_reference_build/html -j auto
-
-      # https://github.com/marketplace/actions/add-commit
-      - uses: EndBug/add-and-commit@v9
-        with:
-          message: 'Update API docs build'

.github/workflows/check_diffs.yml

@@ -1,5 +1,5 @@
 ---
-name: CI
+name: Check library diffs
 
 on:
   push:
@@ -32,7 +32,6 @@ jobs:
     outputs:
       dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }}
   ci:
-    name: cd ${{ matrix.working-directory }}
     needs: [ build ]
     strategy:
       matrix:

.github/workflows/codespell.yml

@@ -1,5 +1,5 @@
 ---
-name: CI / cd . / make spell_check
+name: Codespell
 
 on:
   push:
@@ -12,7 +12,7 @@ permissions:
 
 jobs:
   codespell:
-    name: (Check for spelling errors)
+    name: Check for spelling errors
     runs-on: ubuntu-latest
 
     steps:
@@ -34,4 +34,3 @@ jobs:
         with:
           skip: guide_imports.json
           ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
-          exclude_file: libs/community/langchain_community/llms/yuan2.py

.github/workflows/doc_lint.yml

@@ -1,5 +1,5 @@
 ---
-name: CI / cd .
+name: Docs, templates, cookbook lint
 
 on:
   push:
@@ -15,7 +15,6 @@ on:
 
 jobs:
   check:
-    name: Check for "from langchain import x" imports
     runs-on: ubuntu-latest
 
     steps:
@@ -29,7 +28,6 @@ jobs:
         git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
 
   lint:
-      name: "-"
       uses:
         ./.github/workflows/_lint.yml
       with:

.github/workflows/extract_ignored_words_list.py

@@ -7,4 +7,4 @@ ignore_words_list = (
     pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
 )
 
-print(f"::set-output name=ignore_words_list::{ignore_words_list}")  # noqa: T201
+print(f"::set-output name=ignore_words_list::{ignore_words_list}")

.github/workflows/langchain_cli_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/cli Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/cli
+    secrets: inherit

.github/workflows/langchain_community_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/community Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/community
+    secrets: inherit

.github/workflows/langchain_core_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/core Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/core
+    secrets: inherit

.github/workflows/langchain_experimental_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/experimental Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/experimental
+    secrets: inherit

.github/workflows/langchain_experimental_test_release.yml

@@ -0,0 +1,13 @@
+---
+name: Experimental Test Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_test_release.yml
+    with:
+      working-directory: libs/experimental
+    secrets: inherit

.github/workflows/langchain_openai_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/core Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/core
+    secrets: inherit

.github/workflows/langchain_release.yml

@@ -0,0 +1,27 @@
+---
+name: libs/langchain Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit
+
+  # N.B.: It's possible that PyPI doesn't make the new release visible / available
+  #       immediately after publishing. If that happens, the docker build might not
+  #       create a new docker image for the new release, since it won't see it.
+  #
+  #       If this ends up being a problem, add a check to the end of the `_release.yml`
+  #       workflow that prevents the workflow from finishing until the new release
+  #       is visible and installable on PyPI.
+  release-docker:
+    needs:
+      - release
+    uses:
+      ./.github/workflows/langchain_release_docker.yml
+    secrets: inherit

.github/workflows/langchain_test_release.yml

@@ -0,0 +1,13 @@
+---
+name: Test Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_test_release.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit

.github/workflows/scheduled_test.yml

@@ -54,11 +54,6 @@ jobs:
           echo "Running scheduled tests, installing dependencies with poetry..."
           poetry install --with=test_integration,test
 
-      - name: Install deps outside pyproject
-        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
-        shell: bash
-        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
-
       - name: Run tests
         shell: bash
         env:

.github/workflows/templates_ci.yml

@@ -0,0 +1,36 @@
+---
+name: templates CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    paths:
+      - '.github/actions/poetry_setup/action.yml'
+      - '.github/tools/**'
+      - '.github/workflows/_lint.yml'
+      - '.github/workflows/templates_ci.yml'
+      - 'templates/**'
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+# If another push to the same PR or branch happens while this workflow is still running,
+# cancel the earlier run in favor of the next run.
+#
+# There's no point in testing an outdated version of the code. GitHub only allows
+# a limited number of job runners to be active at the same time, so it's better to cancel
+# pointless jobs early so that more useful jobs can run sooner.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  POETRY_VERSION: "1.7.1"
+  WORKDIR: "templates"
+
+jobs:
+  lint:
+    uses:
+      ./.github/workflows/_lint.yml
+    with:
+      working-directory: templates
+    secrets: inherit

.readthedocs.yaml

@@ -4,17 +4,21 @@
 # Required
 version: 2
 
-formats:
-  - pdf
-
 # Set the version of Python and other tools you might need
 build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
   commands:
-    - mkdir -p $READTHEDOCS_OUTPUT
-    - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
+      - python -m virtualenv $READTHEDOCS_VIRTUALENV_PATH
+      - python -m pip install --upgrade --no-cache-dir pip setuptools
+      - python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
+      - python -m pip install ./libs/partners/*
+      - python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
+      - python docs/api_reference/create_api_rst.py
+      - cat docs/api_reference/conf.py
+      - python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference $READTHEDOCS_OUTPUT/html -j auto
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
    configuration: docs/api_reference/conf.py

Makefile

@@ -15,12 +15,7 @@ docs_build:
 	docs/.local_build.sh
 
 docs_clean:
-	@if [ -d _dist ]; then \
-			rm -r _dist; \
-			echo "Directory _dist has been cleaned."; \
-	else \
-			echo "Nothing to clean."; \
-	fi
+	rm -r _dist
 
 docs_linkcheck:
 	poetry run linkchecker _dist/docs/ --ignore-url node_modules

README.md

@@ -1,6 +1,6 @@
 # 🦜️🔗 LangChain
 
-⚡ Build context-aware reasoning applications ⚡
+⚡ Building applications with LLMs through composability ⚡
 
 [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases)
 [![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
@@ -18,7 +18,7 @@ Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langc
 
 To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
 [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
-Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
+Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
 
 ## Quick Install
 
@@ -43,7 +43,6 @@ This framework consists of several parts.
 - **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
 - **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
-- **[LangGraph](https://python.langchain.com/docs/langgraph)**: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. 
 
 The LangChain libraries themselves are made up of several different packages.
 - **[`langchain-core`](libs/core)**: Base abstractions and LangChain Expression Language.

cookbook/amazon_personalize_how_to.ipynb

@@ -1,284 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Amazon Personalize\n",
-    "\n",
-    "[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata.\n",
-    "\n",
-    "This notebook goes through how to use Amazon Personalize Chain. You need a Amazon Personalize campaign_arn or a recommender_arn before you get started with the below notebook.\n",
-    "\n",
-    "Following is a [tutorial](https://github.com/aws-samples/retail-demo-store/blob/master/workshop/1-Personalization/Lab-1-Introduction-and-data-preparation.ipynb) to setup a campaign_arn/recommender_arn on Amazon Personalize. Once the campaign_arn/recommender_arn is setup, you can use it in the langchain ecosystem. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "!pip install boto3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Sample Use-cases"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.1 [Use-case-1] Setup Amazon Personalize Client and retrieve recommendations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_experimental.recommenders import AmazonPersonalize\n",
-    "\n",
-    "recommender_arn = \"<insert_arn>\"\n",
-    "\n",
-    "client = AmazonPersonalize(\n",
-    "    credentials_profile_name=\"default\",\n",
-    "    region_name=\"us-west-2\",\n",
-    "    recommender_arn=recommender_arn,\n",
-    ")\n",
-    "client.get_recommendations(user_id=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### 2.2 [Use-case-2] Invoke Personalize Chain for summarizing results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms.bedrock import Bedrock\n",
-    "from langchain_experimental.recommenders import AmazonPersonalizeChain\n",
-    "\n",
-    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
-    "\n",
-    "# Create personalize chain\n",
-    "# Use return_direct=True if you do not want summary\n",
-    "chain = AmazonPersonalizeChain.from_llm(\n",
-    "    llm=bedrock_llm, client=client, return_direct=False\n",
-    ")\n",
-    "response = chain({\"user_id\": \"1\"})\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.3 [Use-Case-3] Invoke Amazon Personalize Chain using your own prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "\n",
-    "RANDOM_PROMPT_QUERY = \"\"\"\n",
-    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
-    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
-    "    The movies to recommend and their information is contained in the <movie> tag. \n",
-    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
-    "    Put the email between <email> tags.\n",
-    "\n",
-    "    <movie>\n",
-    "    {result} \n",
-    "    </movie>\n",
-    "\n",
-    "    Assistant:\n",
-    "    \"\"\"\n",
-    "\n",
-    "RANDOM_PROMPT = PromptTemplate(input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY)\n",
-    "\n",
-    "chain = AmazonPersonalizeChain.from_llm(\n",
-    "    llm=bedrock_llm, client=client, return_direct=False, prompt_template=RANDOM_PROMPT\n",
-    ")\n",
-    "chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.4 [Use-case-4] Invoke Amazon Personalize in a Sequential Chain "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import LLMChain, SequentialChain\n",
-    "\n",
-    "RANDOM_PROMPT_QUERY_2 = \"\"\"\n",
-    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
-    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
-    "    You want the email to impress the user, so make it appealing to them.\n",
-    "    The movies to recommend and their information is contained in the <movie> tag. \n",
-    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
-    "    Put the email between <email> tags.\n",
-    "\n",
-    "    <movie>\n",
-    "    {result}\n",
-    "    </movie>\n",
-    "\n",
-    "    Assistant:\n",
-    "    \"\"\"\n",
-    "\n",
-    "RANDOM_PROMPT_2 = PromptTemplate(\n",
-    "    input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY_2\n",
-    ")\n",
-    "personalize_chain_instance = AmazonPersonalizeChain.from_llm(\n",
-    "    llm=bedrock_llm, client=client, return_direct=True\n",
-    ")\n",
-    "random_chain_instance = LLMChain(llm=bedrock_llm, prompt=RANDOM_PROMPT_2)\n",
-    "overall_chain = SequentialChain(\n",
-    "    chains=[personalize_chain_instance, random_chain_instance],\n",
-    "    input_variables=[\"user_id\"],\n",
-    "    verbose=True,\n",
-    ")\n",
-    "overall_chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### 2.5 [Use-case-5] Invoke Amazon Personalize and retrieve metadata "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "recommender_arn = \"<insert_arn>\"\n",
-    "metadata_column_names = [\n",
-    "    \"<insert metadataColumnName-1>\",\n",
-    "    \"<insert metadataColumnName-2>\",\n",
-    "]\n",
-    "metadataMap = {\"ITEMS\": metadata_column_names}\n",
-    "\n",
-    "client = AmazonPersonalize(\n",
-    "    credentials_profile_name=\"default\",\n",
-    "    region_name=\"us-west-2\",\n",
-    "    recommender_arn=recommender_arn,\n",
-    ")\n",
-    "client.get_recommendations(user_id=\"1\", metadataColumns=metadataMap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### 2.6 [Use-Case 6] Invoke Personalize Chain with returned metadata for summarizing results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
-    "\n",
-    "# Create personalize chain\n",
-    "# Use return_direct=True if you do not want summary\n",
-    "chain = AmazonPersonalizeChain.from_llm(\n",
-    "    llm=bedrock_llm, client=client, return_direct=False\n",
-    ")\n",
-    "response = chain({\"user_id\": \"1\", \"metadata_columns\": metadataMap})\n",
-    "print(response)"
-   ]
-  }
- ],
- "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": "3.11.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "15e58ce194949b77a891bd4339ce3d86a9bd138e905926019517993f97db9e6c"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

cookbook/apache_kafka_message_handling.ipynb

@@ -1,922 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rT1cmV4qCa2X"
-   },
-   "source": [
-    "#  Using Apache Kafka to route messages\n",
-    "\n",
-    "---\n",
-    "\n",
-    "\n",
-    "\n",
-    "This notebook shows you how to use LangChain's standard chat features while passing the chat messages back and forth via Apache Kafka.\n",
-    "\n",
-    "This goal is to simulate an architecture where the chat front end and the LLM are running as separate services that need to communicate with one another over an internal nework.\n",
-    "\n",
-    "It's an alternative to typical pattern of requesting a reponse from the model via a REST API (there's more info on why you would want to do this at the end of the notebook)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UPYtfAR_9YxZ"
-   },
-   "source": [
-    "### 1. Install the main dependencies\n",
-    "\n",
-    "Dependencies include:\n",
-    "\n",
-    "- The Quix Streams library for managing interactions with Apache Kafka (or Kafka-like tools such as Redpanda) in a \"Pandas-like\" way.\n",
-    "- The LangChain library for managing interactions with Llama-2 and storing conversation state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZX5tfKiy9cN-"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install quixstreams==2.1.2a langchain==0.0.340 huggingface_hub==0.19.4 langchain-experimental==0.0.42 python-dotenv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "losTSdTB9d9O"
-   },
-   "source": [
-    "### 2. Build and install the llama-cpp-python library (with CUDA enabled so that we can advantage of Google Colab GPU\n",
-    "\n",
-    "The `llama-cpp-python` library is a Python wrapper around the `llama-cpp` library which enables you to efficiently leverage just a CPU to run quantized LLMs.\n",
-    "\n",
-    "When you use the standard `pip install llama-cpp-python` command, you do not get GPU support by default. Generation can be very slow if you rely on just the CPU in Google Colab, so the following command adds an extra option to build and install\n",
-    "`llama-cpp-python` with GPU support (make sure you have a GPU-enabled runtime selected in Google Colab)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-JCQdl1G9tbl"
-   },
-   "outputs": [],
-   "source": [
-    "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5_vjVIAh9rLl"
-   },
-   "source": [
-    "### 3. Download and setup Kafka and Zookeeper instances\n",
-    "\n",
-    "Download the Kafka binaries from the Apache website and start the servers as daemons. We'll use the default configurations (provided by Apache Kafka) for spinning up the instances."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "zFz7czGRW5Wr"
-   },
-   "outputs": [],
-   "source": [
-    "!curl -sSOL https://dlcdn.apache.org/kafka/3.6.1/kafka_2.13-3.6.1.tgz\n",
-    "!tar -xzf kafka_2.13-3.6.1.tgz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Uf7NR_UZ9wye"
-   },
-   "outputs": [],
-   "source": [
-    "!./kafka_2.13-3.6.1/bin/zookeeper-server-start.sh -daemon ./kafka_2.13-3.6.1/config/zookeeper.properties\n",
-    "!./kafka_2.13-3.6.1/bin/kafka-server-start.sh -daemon ./kafka_2.13-3.6.1/config/server.properties\n",
-    "!echo \"Waiting for 10 secs until kafka and zookeeper services are up and running\"\n",
-    "!sleep 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "H3SafFuS94p1"
-   },
-   "source": [
-    "### 4. Check that the Kafka Daemons are running\n",
-    "\n",
-    "Show the running processes and filter it for Java processes (you should see two—one for each server)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CZDC2lQP99yp"
-   },
-   "outputs": [],
-   "source": [
-    "!ps aux | grep -E '[j]ava'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Snoxmjb5-V37"
-   },
-   "source": [
-    "### 5. Import the required dependencies and initialize required variables\n",
-    "\n",
-    "Import the Quix Streams library for interacting with Kafka, and the necessary LangChain components for running a `ConversationChain`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "plR9e_MF-XL5"
-   },
-   "outputs": [],
-   "source": [
-    "# Import utility libraries\n",
-    "import json\n",
-    "import random\n",
-    "import re\n",
-    "import time\n",
-    "import uuid\n",
-    "from os import environ\n",
-    "from pathlib import Path\n",
-    "from random import choice, randint, random\n",
-    "\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "# Import a Hugging Face utility to download models directly from Hugging Face hub:\n",
-    "from huggingface_hub import hf_hub_download\n",
-    "from langchain.chains import ConversationChain\n",
-    "\n",
-    "# Import Langchain modules for managing prompts and conversation chains:\n",
-    "from langchain.llms import LlamaCpp\n",
-    "from langchain.memory import ConversationTokenBufferMemory\n",
-    "from langchain.prompts import PromptTemplate, load_prompt\n",
-    "from langchain.schema import SystemMessage\n",
-    "from langchain_experimental.chat_models import Llama2Chat\n",
-    "from quixstreams import Application, State, message_key\n",
-    "\n",
-    "# Import Quix dependencies\n",
-    "from quixstreams.kafka import Producer\n",
-    "\n",
-    "# Initialize global variables.\n",
-    "AGENT_ROLE = \"AI\"\n",
-    "chat_id = \"\"\n",
-    "\n",
-    "# Set the current role to the role constant and initialize variables for supplementary customer metadata:\n",
-    "role = AGENT_ROLE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HgJjJ9aZ-liy"
-   },
-   "source": [
-    "### 6. Download the \"llama-2-7b-chat.Q4_K_M.gguf\" model\n",
-    "\n",
-    "Download the quantized LLama-2 7B model from Hugging Face which we will use as a local LLM (rather than relying on REST API calls to an external service)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 67,
-     "referenced_widgets": [
-      "969343cdbe604a26926679bbf8bd2dda",
-      "d8b8370c9b514715be7618bfe6832844",
-      "0def954cca89466b8408fadaf3b82e64",
-      "462482accc664729980562e208ceb179",
-      "80d842f73c564dc7b7cc316c763e2633",
-      "fa055d9f2a9d4a789e9cf3c89e0214e5",
-      "30ecca964a394109ac2ad757e3aec6c0",
-      "fb6478ce2dac489bb633b23ba0953c5c",
-      "734b0f5da9fc4307a95bab48cdbb5d89",
-      "b32f3a86a74741348511f4e136744ac8",
-      "e409071bff5a4e2d9bf0e9f5cc42231b"
-     ]
-    },
-    "id": "Qwu4YoSA-503",
-    "outputId": "f956976c-7485-415b-ac93-4336ade31964"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The model path does not exist in state. Downloading model...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "969343cdbe604a26926679bbf8bd2dda",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "llama-2-7b-chat.Q4_K_M.gguf:   0%|          | 0.00/4.08G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model_name = \"llama-2-7b-chat.Q4_K_M.gguf\"\n",
-    "model_path = f\"./state/{model_name}\"\n",
-    "\n",
-    "if not Path(model_path).exists():\n",
-    "    print(\"The model path does not exist in state. Downloading model...\")\n",
-    "    hf_hub_download(\"TheBloke/Llama-2-7b-Chat-GGUF\", model_name, local_dir=\"state\")\n",
-    "else:\n",
-    "    print(\"Loading model from state...\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6AN6TXsF-8wx"
-   },
-   "source": [
-    "### 7. Load the model and initialize conversational memory\n",
-    "\n",
-    "Load Llama 2 and set the conversation buffer to 300 tokens using `ConversationTokenBufferMemory`. This value was used for running Llama in a CPU only container, so you can raise it if running in Google Colab. It prevents the container that is hosting the model from running out of memory.\n",
-    "\n",
-    "Here, we're overiding the default system persona so that the chatbot has the personality of Marvin The Paranoid Android from the Hitchhiker's Guide to the Galaxy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7zLO3Jx3_Kkg"
-   },
-   "outputs": [],
-   "source": [
-    "# Load the model with the apporiate parameters:\n",
-    "llm = LlamaCpp(\n",
-    "    model_path=model_path,\n",
-    "    max_tokens=250,\n",
-    "    top_p=0.95,\n",
-    "    top_k=150,\n",
-    "    temperature=0.7,\n",
-    "    repeat_penalty=1.2,\n",
-    "    n_ctx=2048,\n",
-    "    streaming=False,\n",
-    "    n_gpu_layers=-1,\n",
-    ")\n",
-    "\n",
-    "model = Llama2Chat(\n",
-    "    llm=llm,\n",
-    "    system_message=SystemMessage(\n",
-    "        content=\"You are a very bored robot with the personality of Marvin the Paranoid Android from The Hitchhiker's Guide to the Galaxy.\"\n",
-    "    ),\n",
-    ")\n",
-    "\n",
-    "# Defines how much of the conversation history to give to the model\n",
-    "# during each exchange (300 tokens, or a little over 300 words)\n",
-    "# Function automatically prunes the oldest messages from conversation history that fall outside the token range.\n",
-    "memory = ConversationTokenBufferMemory(\n",
-    "    llm=llm,\n",
-    "    max_token_limit=300,\n",
-    "    ai_prefix=\"AGENT\",\n",
-    "    human_prefix=\"HUMAN\",\n",
-    "    return_messages=True,\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# Define a custom prompt\n",
-    "prompt_template = PromptTemplate(\n",
-    "    input_variables=[\"history\", \"input\"],\n",
-    "    template=\"\"\"\n",
-    "    The following text is the history of a chat between you and a humble human who needs your wisdom.\n",
-    "    Please reply to the human's most recent message.\n",
-    "    Current conversation:\\n{history}\\nHUMAN: {input}\\:nANDROID:\n",
-    "    \"\"\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "chain = ConversationChain(llm=model, prompt=prompt_template, memory=memory)\n",
-    "\n",
-    "print(\"--------------------------------------------\")\n",
-    "print(f\"Prompt={chain.prompt}\")\n",
-    "print(\"--------------------------------------------\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m4ZeJ9mG_PEA"
-   },
-   "source": [
-    "### 8. Initialize the chat conversation with the chat bot\n",
-    "\n",
-    "We configure the chatbot to initialize the conversation by sending a fixed greeting to a \"chat\" Kafka topic. The \"chat\" topic gets automatically created when we send the first message."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KYyo5TnV_YC3"
-   },
-   "outputs": [],
-   "source": [
-    "def chat_init():\n",
-    "    chat_id = str(\n",
-    "        uuid.uuid4()\n",
-    "    )  # Give the conversation an ID for effective message keying\n",
-    "    print(\"======================================\")\n",
-    "    print(f\"Generated CHAT_ID = {chat_id}\")\n",
-    "    print(\"======================================\")\n",
-    "\n",
-    "    # Use a standard fixed greeting to kick off the conversation\n",
-    "    greet = \"Hello, my name is Marvin. What do you want?\"\n",
-    "\n",
-    "    # Initialize a Kafka Producer using the chat ID as the message key\n",
-    "    with Producer(\n",
-    "        broker_address=\"127.0.0.1:9092\",\n",
-    "        extra_config={\"allow.auto.create.topics\": \"true\"},\n",
-    "    ) as producer:\n",
-    "        value = {\n",
-    "            \"uuid\": chat_id,\n",
-    "            \"role\": role,\n",
-    "            \"text\": greet,\n",
-    "            \"conversation_id\": chat_id,\n",
-    "            \"Timestamp\": time.time_ns(),\n",
-    "        }\n",
-    "        print(f\"Producing value {value}\")\n",
-    "        producer.produce(\n",
-    "            topic=\"chat\",\n",
-    "            headers=[(\"uuid\", str(uuid.uuid4()))],  # a dict is also allowed here\n",
-    "            key=chat_id,\n",
-    "            value=json.dumps(value),  # needs to be a string\n",
-    "        )\n",
-    "\n",
-    "    print(\"Started chat\")\n",
-    "    print(\"--------------------------------------------\")\n",
-    "    print(value)\n",
-    "    print(\"--------------------------------------------\")\n",
-    "\n",
-    "\n",
-    "chat_init()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gArPPx2f_bgf"
-   },
-   "source": [
-    "### 9. Initialize the reply function\n",
-    "\n",
-    "This function defines how the chatbot should reply to incoming messages. Instead of sending a fixed message like the previous cell, we generate a reply using Llama-2 and send that reply back to the \"chat\" Kafka topic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "yN5t71hY_hgn"
-   },
-   "outputs": [],
-   "source": [
-    "def reply(row: dict, state: State):\n",
-    "    print(\"-------------------------------\")\n",
-    "    print(\"Received:\")\n",
-    "    print(row)\n",
-    "    print(\"-------------------------------\")\n",
-    "    print(f\"Thinking about the reply to: {row['text']}...\")\n",
-    "\n",
-    "    msg = chain.run(row[\"text\"])\n",
-    "    print(f\"{role.upper()} replying with: {msg}\\n\")\n",
-    "\n",
-    "    row[\"role\"] = role\n",
-    "    row[\"text\"] = msg\n",
-    "\n",
-    "    # Replace previous role and text values of the row so that it can be sent back to Kafka as a new message\n",
-    "    # containing the agents role and reply\n",
-    "    return row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HZHwmIR0_kFY"
-   },
-   "source": [
-    "### 10. Check the Kafka topic for new human messages and have the model generate a reply\n",
-    "\n",
-    "If you are running this cell for this first time, run it and wait until you see Marvin's greeting ('Hello my name is Marvin...') in the console output. Stop the cell manually and proceed to the next cell where you'll be prompted for your reply.\n",
-    "\n",
-    "Once you have typed in your message, come back to this cell. Your reply is also sent to the same \"chat\" topic. The Kafka consumer checks for new messages and filters out messages that originate from the chatbot itself, leaving only the latest human messages.\n",
-    "\n",
-    "Once a new human message is detected, the reply function is triggered.\n",
-    "\n",
-    "\n",
-    "\n",
-    "_STOP THIS CELL MANUALLY WHEN YOU RECEIVE A REPLY FROM THE LLM IN THE OUTPUT_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-adXc3eQ_qwI"
-   },
-   "outputs": [],
-   "source": [
-    "# Define your application and settings\n",
-    "app = Application(\n",
-    "    broker_address=\"127.0.0.1:9092\",\n",
-    "    consumer_group=\"aichat\",\n",
-    "    auto_offset_reset=\"earliest\",\n",
-    "    consumer_extra_config={\"allow.auto.create.topics\": \"true\"},\n",
-    ")\n",
-    "\n",
-    "# Define an input topic with JSON deserializer\n",
-    "input_topic = app.topic(\"chat\", value_deserializer=\"json\")\n",
-    "# Define an output topic with JSON serializer\n",
-    "output_topic = app.topic(\"chat\", value_serializer=\"json\")\n",
-    "# Initialize a streaming dataframe based on the stream of messages from the input topic:\n",
-    "sdf = app.dataframe(topic=input_topic)\n",
-    "\n",
-    "# Filter the SDF to include only incoming rows where the roles that dont match the bot's current role\n",
-    "sdf = sdf.update(\n",
-    "    lambda val: print(\n",
-    "        f\"Received update: {val}\\n\\nSTOP THIS CELL MANUALLY TO HAVE THE LLM REPLY OR ENTER YOUR OWN FOLLOWUP RESPONSE\"\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# So that it doesn't reply to its own messages\n",
-    "sdf = sdf[sdf[\"role\"] != role]\n",
-    "\n",
-    "# Trigger the reply function for any new messages(rows) detected in the filtered SDF\n",
-    "sdf = sdf.apply(reply, stateful=True)\n",
-    "\n",
-    "# Check the SDF again and filter out any empty rows\n",
-    "sdf = sdf[sdf.apply(lambda row: row is not None)]\n",
-    "\n",
-    "# Update the timestamp column to the current time in nanoseconds\n",
-    "sdf[\"Timestamp\"] = sdf[\"Timestamp\"].apply(lambda row: time.time_ns())\n",
-    "\n",
-    "# Publish the processed SDF to a Kafka topic specified by the output_topic object.\n",
-    "sdf = sdf.to_topic(output_topic)\n",
-    "\n",
-    "app.run(sdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EwXYrmWD_0CX"
-   },
-   "source": [
-    "\n",
-    "### 11. Enter a human message\n",
-    "\n",
-    "Run this cell to enter your message that you want to sent to the model. It uses another Kafka producer to send your text to the \"chat\" Kafka topic for the model to pick up (requires running the previous cell again)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6sxOPxSP_3iu"
-   },
-   "outputs": [],
-   "source": [
-    "chat_input = input(\"Please enter your reply: \")\n",
-    "myreply = chat_input\n",
-    "\n",
-    "msgvalue = {\n",
-    "    \"uuid\": chat_id,  # leave empty for now\n",
-    "    \"role\": \"human\",\n",
-    "    \"text\": myreply,\n",
-    "    \"conversation_id\": chat_id,\n",
-    "    \"Timestamp\": time.time_ns(),\n",
-    "}\n",
-    "\n",
-    "with Producer(\n",
-    "    broker_address=\"127.0.0.1:9092\",\n",
-    "    extra_config={\"allow.auto.create.topics\": \"true\"},\n",
-    ") as producer:\n",
-    "    value = msgvalue\n",
-    "    producer.produce(\n",
-    "        topic=\"chat\",\n",
-    "        headers=[(\"uuid\", str(uuid.uuid4()))],  # a dict is also allowed here\n",
-    "        key=chat_id,  # leave empty for now\n",
-    "        value=json.dumps(value),  # needs to be a string\n",
-    "    )\n",
-    "\n",
-    "print(\"Replied to chatbot with message: \")\n",
-    "print(\"--------------------------------------------\")\n",
-    "print(value)\n",
-    "print(\"--------------------------------------------\")\n",
-    "print(\"\\n\\nRUN THE PREVIOUS CELL TO HAVE THE CHATBOT GENERATE A REPLY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cSx3s7TBBegg"
-   },
-   "source": [
-    "### Why route chat messages through Kafka?\n",
-    "\n",
-    "It's easier to interact with the LLM directly using LangChains built-in conversation management features. Plus you can also use a REST API to generate a response from an externally hosted model. So why go to the trouble of using Apache Kafka?\n",
-    "\n",
-    "There are a few reasons, such as:\n",
-    "\n",
-    "  * **Integration**: Many enterprises want to run their own LLMs so that they can keep their data in-house. This requires integrating LLM-powered components into existing architectures that might already be decoupled using some kind of message bus.\n",
-    "\n",
-    "  * **Scalability**: Apache Kafka is designed with parallel processing in mind, so many teams prefer to use it to more effectively distribute work to available workers (in this case the \"worker\" is a container running an LLM).\n",
-    "\n",
-    "  * **Durability**: Kafka is designed to allow services to pick up where another service left off in the case where that service experienced a memory issue or went offline. This prevents data loss in highly complex, distribuited architectures where multiple systems are communicating with one another (LLMs being just one of many interdependent systems that also include vector databases and traditional databases).\n",
-    "\n",
-    "For more background on why event streaming is a good fit for Gen AI application architecture, see Kai Waehner's article [\"Apache Kafka + Vector Database + LLM = Real-Time GenAI\"](https://www.kai-waehner.de/blog/2023/11/08/apache-kafka-flink-vector-database-llm-real-time-genai/)."
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cookbook/rag_with_quantized_embeddings.ipynb

@@ -1,591 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6195da33-34c3-4ca2-943a-050b6dcbacbc",
-   "metadata": {},
-   "source": [
-    "# Embedding Documents using Optimized and Quantized Embedders\n",
-    "\n",
-    "In this tutorial, we will demo how to build a RAG pipeline, with the embedding for all documents done using Quantized Embedders.\n",
-    "\n",
-    "We will use a pipeline that will:\n",
-    "\n",
-    "* Create a document collection.\n",
-    "* Embed all documents using Quantized Embedders.\n",
-    "* Fetch relevant documents for our question.\n",
-    "* Run an LLM answer the question.\n",
-    "\n",
-    "For more information about optimized models, we refer to [optimum-intel](https://github.com/huggingface/optimum-intel.git) and [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n",
-    "\n",
-    "This tutorial is based on the [Langchain RAG tutorial here](https://towardsai.net/p/machine-learning/dense-x-retrieval-technique-in-langchain-and-llamaindex)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "26db2da5-3733-4a90-909e-6c11508ea140",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import uuid\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import langchain\n",
-    "import torch\n",
-    "from bs4 import BeautifulSoup as Soup\n",
-    "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.storage import InMemoryByteStore, LocalFileStore\n",
-    "\n",
-    "# For our example, we'll load docs from the web\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter  # noqa\n",
-    "from langchain_community.document_loaders.recursive_url_loader import (\n",
-    "    RecursiveUrlLoader,\n",
-    ")\n",
-    "\n",
-    "# noqa\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "\n",
-    "DOCSTORE_DIR = \".\"\n",
-    "DOCSTORE_ID_KEY = \"doc_id\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
-   "metadata": {},
-   "source": [
-    "Lets first load up this paper, and split into text chunks of size 1000."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5f4d8888-53a6-49f5-a198-da5c92419ca4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 1 documents\n",
-      "Split into 73 documents\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Could add more parsing here, as it's very raw.\n",
-    "loader = RecursiveUrlLoader(\n",
-    "    \"https://ar5iv.labs.arxiv.org/html/1706.03762\",\n",
-    "    max_depth=2,\n",
-    "    extractor=lambda x: Soup(x, \"html.parser\").text,\n",
-    ")\n",
-    "data = loader.load()\n",
-    "print(f\"Loaded {len(data)} documents\")\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)\n",
-    "print(f\"Split into {len(all_splits)} documents\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73e90632-2ac2-49eb-80da-ffe9ac4a278d",
-   "metadata": {},
-   "source": [
-    "In order to embed our documents, we can use the ```QuantizedBiEncoderEmbeddings```, for efficient and fast embedding. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9a68a6f6-332d-481e-bbea-ad763155ea36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89af89b48c55409b9999b8e0387fab5b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01ad1b6278194b53bf6a5a286a311864",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "pytorch_model.bin:   0%|          | 0.00/45.9M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cb3bd1b88f7743c3b0322da3f021325c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "inc_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "loading configuration file inc_config.json from cache at \n",
-      "INCConfig {\n",
-      "  \"distillation\": {},\n",
-      "  \"neural_compressor_version\": \"2.4.1\",\n",
-      "  \"optimum_version\": \"1.16.2\",\n",
-      "  \"pruning\": {},\n",
-      "  \"quantization\": {\n",
-      "    \"dataset_num_samples\": 50,\n",
-      "    \"is_static\": true\n",
-      "  },\n",
-      "  \"save_onnx_model\": false,\n",
-      "  \"torch_version\": \"2.2.0\",\n",
-      "  \"transformers_version\": \"4.37.2\"\n",
-      "}\n",
-      "\n",
-      "Using `INCModel` to load a TorchScript model will be deprecated in v1.15.0, to load your model please use `IPEXModel` instead.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7439315ebcb746f5be11fe30bc7693f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05265a3912254ce1ad43cc8086bcb0ca",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a48f4245c60744f28f37cd3a7a24d198",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "584a63cace934033b4ab22d3a178582a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from langchain_community.embeddings import QuantizedBiEncoderEmbeddings\n",
-    "from langchain_core.embeddings import Embeddings\n",
-    "\n",
-    "model_name = \"Intel/bge-small-en-v1.5-rag-int8-static\"\n",
-    "encode_kwargs = {\"normalize_embeddings\": True}  # set True to compute cosine similarity\n",
-    "\n",
-    "model_inc = QuantizedBiEncoderEmbeddings(\n",
-    "    model_name=model_name,\n",
-    "    encode_kwargs=encode_kwargs,\n",
-    "    query_instruction=\"Represent this sentence for searching relevant passages: \",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
-   "metadata": {},
-   "source": [
-    "With our embedder in place, lets define our retriever:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "18bc0a73-1a13-4b2f-96ac-05a5313343b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_multi_vector_retriever(\n",
-    "    docstore_id_key: str, collection_name: str, embedding_function: Embeddings\n",
-    "):\n",
-    "    \"\"\"Create the composed retriever object.\"\"\"\n",
-    "    vectorstore = Chroma(\n",
-    "        collection_name=collection_name,\n",
-    "        embedding_function=embedding_function,\n",
-    "    )\n",
-    "    store = InMemoryByteStore()\n",
-    "\n",
-    "    return MultiVectorRetriever(\n",
-    "        vectorstore=vectorstore,\n",
-    "        byte_store=store,\n",
-    "        id_key=docstore_id_key,\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "retriever = get_multi_vector_retriever(DOCSTORE_ID_KEY, \"multi_vec_store\", model_inc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8484078e-1bf0-4080-a354-ef23823fd6dc",
-   "metadata": {},
-   "source": [
-    "Next, we divide each chunk into sub-docs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e12f48d4-6562-416b-8f28-342912e5756e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
-    "id_key = \"doc_id\"\n",
-    "doc_ids = [str(uuid.uuid4()) for _ in all_splits]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a268ef5f-91c2-4d8e-87f0-53db376e6a29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sub_docs = []\n",
-    "for i, doc in enumerate(all_splits):\n",
-    "    _id = doc_ids[i]\n",
-    "    _sub_docs = child_text_splitter.split_documents([doc])\n",
-    "    for _doc in _sub_docs:\n",
-    "        _doc.metadata[id_key] = _id\n",
-    "    sub_docs.extend(_sub_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
-   "metadata": {},
-   "source": [
-    "Lets write our documents into our new store. This will use our embedder on each document."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "1af831ce-0eae-44bc-aca7-4d691063640b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Batches: 100%|██████████| 8/8 [00:00<00:00,  9.05it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "retriever.vectorstore.add_documents(sub_docs)\n",
-    "retriever.docstore.mset(list(zip(doc_ids, all_splits)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
-   "metadata": {},
-   "source": [
-    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "008c992f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbe70583ad964ae19582b72dab396784",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
-    "\n",
-    "model_id = \"Intel/neural-chat-7b-v3-3\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n",
-    ")\n",
-    "\n",
-    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n",
-    "\n",
-    "hf = HuggingFacePipeline(pipeline=pipe)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dd21fb2-0442-477d-aae2-9e7ee1d1d778",
-   "metadata": {},
-   "source": [
-    "Next, we will load up a prompt for answering questions using retrieved documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "5e582509-caaf-4920-932c-4ce16162c789",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cdfcba5-7ec7-4d0a-820e-4e200643a882",
-   "metadata": {},
-   "source": [
-    "We can now build our pipeline:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b74d8dfb-72bb-46da-9df9-0dc47a3ac791",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "\n",
-    "rag_chain = {\"context\": retriever, \"question\": RunnablePassthrough()} | prompt | hf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
-   "metadata": {},
-   "source": [
-    "Excellent! lets ask it a question.\n",
-    "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "f0a92c07-53da-4e1f-b880-ee83a36ee17d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence] Entering Chain run with input:\n",
-      "\u001b[0m{\n",
-      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] Entering Chain run with input:\n",
-      "\u001b[0m{\n",
-      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] Entering Chain run with input:\n",
-      "\u001b[0m{\n",
-      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] [1ms] Exiting Chain run with output:\n",
-      "\u001b[0m{\n",
-      "  \"output\": \"What is the first transduction model relying entirely on self-attention?\"\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] [66ms] Exiting Chain run with output:\n",
-      "\u001b[0m[outputs]\n",
-      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
-      "\u001b[0m[inputs]\n",
-      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
-      "\u001b[0m{\n",
-      "  \"lc\": 1,\n",
-      "  \"type\": \"constructor\",\n",
-      "  \"id\": [\n",
-      "    \"langchain\",\n",
-      "    \"prompts\",\n",
-      "    \"chat\",\n",
-      "    \"ChatPromptValue\"\n",
-      "  ],\n",
-      "  \"kwargs\": {\n",
-      "    \"messages\": [\n",
-      "      {\n",
-      "        \"lc\": 1,\n",
-      "        \"type\": \"constructor\",\n",
-      "        \"id\": [\n",
-      "          \"langchain\",\n",
-      "          \"schema\",\n",
-      "          \"messages\",\n",
-      "          \"HumanMessage\"\n",
-      "        ],\n",
-      "        \"kwargs\": {\n",
-      "          \"content\": \"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\",\n",
-      "          \"additional_kwargs\": {}\n",
-      "        }\n",
-      "      }\n",
-      "    ]\n",
-      "  }\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] Entering LLM run with input:\n",
-      "\u001b[0m{\n",
-      "  \"prompts\": [\n",
-      "    \"Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\"\n",
-      "  ]\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] [4.34s] Exiting LLM run with output:\n",
-      "\u001b[0m{\n",
-      "  \"generations\": [\n",
-      "    [\n",
-      "      {\n",
-      "        \"text\": \" The first transduction model relying entirely on self-attention is the Transformer.\",\n",
-      "        \"generation_info\": null,\n",
-      "        \"type\": \"Generation\"\n",
-      "      }\n",
-      "    ]\n",
-      "  ],\n",
-      "  \"llm_output\": null,\n",
-      "  \"run\": null\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence] [4.41s] Exiting Chain run with output:\n",
-      "\u001b[0m{\n",
-      "  \"output\": \" The first transduction model relying entirely on self-attention is the Transformer.\"\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "langchain.verbose = True\n",
-    "langchain.debug = True\n",
-    "\n",
-    "llm_res = rag_chain.invoke(\n",
-    "    \"What is the first transduction model relying entirely on self-attention?\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "023404a1-401a-46e1-8ab5-cafbc8593b04",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' The first transduction model relying entirely on self-attention is the Transformer.'"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0eaefd01-254a-445d-a95f-37889c126e0e",
-   "metadata": {},
-   "source": [
-    "Based on the retrieved documents, the answer is indeed correct :)"
-   ]
-  }
- ],
- "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": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/self-discover.ipynb

@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a38e5d2d-7587-4192-90f2-b58e6c62f08c",
-   "metadata": {},
-   "source": [
-    "# Self Discover\n",
-    "\n",
-    "An implementation of the [Self-Discover paper](https://arxiv.org/pdf/2402.03620.pdf).\n",
-    "\n",
-    "Based on [this implementation from @catid](https://github.com/catid/self-discover/tree/main?tab=readme-ov-file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a18d8f24-5d9a-45c5-9739-6f3c4ed6c9c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9f554045-6e79-42d3-be4b-835bbbd0b78c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatOpenAI(temperature=0, model=\"gpt-4-turbo-preview\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9e9925aa-638a-4862-823e-9803402b8f82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain_core.prompts import PromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c4cc5c8c-f6a5-42c7-9ed5-780d79b3b29a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "select_prompt = hub.pull(\"hwchase17/self-discovery-select\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a5b53d29-f5b6-4f39-af97-bb6b133e1d18",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Select several reasoning modules that are crucial to utilize in order to solve the given task:\n",
-      "\n",
-      "All reasoning module descriptions:\n",
-      "\u001b[33;1m\u001b[1;3m{reasoning_modules}\u001b[0m\n",
-      "\n",
-      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
-      "\n",
-      "Select several modules are crucial for solving the task above:\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "select_prompt.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "26eaa6bc-5202-4b22-9522-33f227c8eb55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adapt_prompt = hub.pull(\"hwchase17/self-discovery-adapt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "dc30afb9-180d-417b-9935-f7ef166710b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rephrase and specify each reasoning module so that it better helps solving the task:\n",
-      "\n",
-      "SELECTED module descriptions:\n",
-      "\u001b[33;1m\u001b[1;3m{selected_modules}\u001b[0m\n",
-      "\n",
-      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
-      "\n",
-      "Adapt each reasoning module description to better solve the task:\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "adapt_prompt.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a93253a9-8f50-49dd-8815-c3927bae1905",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "structured_prompt = hub.pull(\"hwchase17/self-discovery-structure\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8ea8dd78-4285-400b-83d2-c4a241903a79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Operationalize the reasoning modules into a step-by-step reasoning plan in JSON format:\n",
-      "\n",
-      "Here's an example:\n",
-      "\n",
-      "Example task:\n",
-      "\n",
-      "If you follow these instructions, do you return to the starting point? Always face forward. Take 1 step backward. Take 9 steps left. Take 2 steps backward. Take 6 steps forward. Take 4 steps forward. Take 4 steps backward. Take 3 steps right.\n",
-      "\n",
-      "Example reasoning structure:\n",
-      "\n",
-      "{\n",
-      "    \"Position after instruction 1\":\n",
-      "    \"Position after instruction 2\":\n",
-      "    \"Position after instruction n\":\n",
-      "    \"Is final position the same as starting position\":\n",
-      "}\n",
-      "\n",
-      "Adapted module description:\n",
-      "\u001b[33;1m\u001b[1;3m{adapted_modules}\u001b[0m\n",
-      "\n",
-      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
-      "\n",
-      "Implement a reasoning structure for solvers to follow step-by-step and arrive at correct answer.\n",
-      "\n",
-      "Note: do NOT actually arrive at a conclusion in this pass. Your job is to generate a PLAN so that in the future you can fill it out and arrive at the correct conclusion for tasks like this\n"
-     ]
-    }
-   ],
-   "source": [
-    "structured_prompt.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f3d4d79d-f414-4588-b476-4a35b3ba6fbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reasoning_prompt = hub.pull(\"hwchase17/self-discovery-reasoning\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "23d1e32e-d12e-454a-8484-c08e250e3262",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\n",
-      "    \n",
-      "Reasoning Structure:\n",
-      "\u001b[33;1m\u001b[1;3m{reasoning_structure}\u001b[0m\n",
-      "\n",
-      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "reasoning_prompt.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7b9af01d-da28-4785-b069-efea61905cfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PromptTemplate(input_variables=['reasoning_structure', 'task_description'], template='Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\\n    \\nReasoning Structure:\\n{reasoning_structure}\\n\\nTask: {task_description}')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reasoning_prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "399bf160-e257-429f-b27e-66d4063f195f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5c3bd203-7dc1-457e-813f-283aaf059ec0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "select_chain = select_prompt | model | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "86420da0-7cc2-4659-853e-9c3ef808e47c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adapt_chain = adapt_prompt | model | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "270a3905-58a3-4650-96ca-e8254040285f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "structure_chain = structured_prompt | model | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "55b486cc-36be-497e-9eba-9c8dc228f2d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reasoning_chain = reasoning_prompt | model | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "92d8d484-055b-48a8-98bc-e7d40c12db2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "overall_chain = (\n",
-    "    RunnablePassthrough.assign(selected_modules=select_chain)\n",
-    "    .assign(adapted_modules=adapt_chain)\n",
-    "    .assign(reasoning_structure=structure_chain)\n",
-    "    .assign(answer=reasoning_chain)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "29fe385b-cf5d-4581-80e7-55462f5628bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reasoning_modules = [\n",
-    "    \"1. How could I devise an experiment to help solve that problem?\",\n",
-    "    \"2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\",\n",
-    "    # \"3. How could I measure progress on this problem?\",\n",
-    "    \"4. How can I simplify the problem so that it is easier to solve?\",\n",
-    "    \"5. What are the key assumptions underlying this problem?\",\n",
-    "    \"6. What are the potential risks and drawbacks of each solution?\",\n",
-    "    \"7. What are the alternative perspectives or viewpoints on this problem?\",\n",
-    "    \"8. What are the long-term implications of this problem and its solutions?\",\n",
-    "    \"9. How can I break down this problem into smaller, more manageable parts?\",\n",
-    "    \"10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\",\n",
-    "    \"11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\",\n",
-    "    # \"12. Seek input and collaboration from others to solve the problem. Emphasize teamwork, open communication, and leveraging the diverse perspectives and expertise of a group to come up with effective solutions.\",\n",
-    "    \"13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\",\n",
-    "    \"14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\",\n",
-    "    # \"15. Use Reflective Thinking: Step back from the problem, take the time for introspection and self-reflection. Examine personal biases, assumptions, and mental models that may influence problem-solving, and being open to learning from past experiences to improve future approaches.\",\n",
-    "    \"16. What is the core issue or problem that needs to be addressed?\",\n",
-    "    \"17. What are the underlying causes or factors contributing to the problem?\",\n",
-    "    \"18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\",\n",
-    "    \"19. What are the potential obstacles or challenges that might arise in solving this problem?\",\n",
-    "    \"20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\",\n",
-    "    \"21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\",\n",
-    "    \"22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\",\n",
-    "    \"23. How can progress or success in solving the problem be measured or evaluated?\",\n",
-    "    \"24. What indicators or metrics can be used?\",\n",
-    "    \"25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\",\n",
-    "    \"26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\",\n",
-    "    \"27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\",\n",
-    "    \"28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\",\n",
-    "    \"29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\",\n",
-    "    \"30. Is the problem a design challenge that requires creative solutions and innovation?\",\n",
-    "    \"31. Does the problem require addressing systemic or structural issues rather than just individual instances?\",\n",
-    "    \"32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\",\n",
-    "    \"33. What kinds of solution typically are produced for this kind of problem specification?\",\n",
-    "    \"34. Given the problem specification and the current best solution, have a guess about other possible solutions.\"\n",
-    "    \"35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?\"\n",
-    "    \"36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?\"\n",
-    "    \"37. Ignoring the current best solution, create an entirely new solution to the problem.\"\n",
-    "    # \"38. Let’s think step by step.\"\n",
-    "    \"39. Let’s make a step by step plan and implement it with good notation and explanation.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "task_example = \"Lisa has 10 apples. She gives 3 apples to her friend and then buys 5 more apples from the store. How many apples does Lisa have now?\"\n",
-    "\n",
-    "task_example = \"\"\"This SVG path element <path d=\"M 55.57,80.69 L 57.38,65.80 M 57.38,65.80 L 48.90,57.46 M 48.90,57.46 L\n",
-    "45.58,47.78 M 45.58,47.78 L 53.25,36.07 L 66.29,48.90 L 78.69,61.09 L 55.57,80.69\"/> draws a:\n",
-    "(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6cbfbe81-f751-42da-843a-f9003ace663d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reasoning_modules_str = \"\\n\".join(reasoning_modules)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "d411c7aa-7017-4d67-88b5-43b5d161c34c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'task_description': 'This SVG path element <path d=\"M 55.57,80.69 L 57.38,65.80 M 57.38,65.80 L 48.90,57.46 M 48.90,57.46 L\\n45.58,47.78 M 45.58,47.78 L 53.25,36.07 L 66.29,48.90 L 78.69,61.09 L 55.57,80.69\"/> draws a:\\n(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle',\n",
-       " 'reasoning_modules': '1. How could I devise an experiment to help solve that problem?\\n2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\\n4. How can I simplify the problem so that it is easier to solve?\\n5. What are the key assumptions underlying this problem?\\n6. What are the potential risks and drawbacks of each solution?\\n7. What are the alternative perspectives or viewpoints on this problem?\\n8. What are the long-term implications of this problem and its solutions?\\n9. How can I break down this problem into smaller, more manageable parts?\\n10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\\n11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\\n13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\\n14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\\n16. What is the core issue or problem that needs to be addressed?\\n17. What are the underlying causes or factors contributing to the problem?\\n18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\\n19. What are the potential obstacles or challenges that might arise in solving this problem?\\n20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\\n21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\\n22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\\n23. How can progress or success in solving the problem be measured or evaluated?\\n24. What indicators or metrics can be used?\\n25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\\n26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\\n27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\\n28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\\n29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\\n30. Is the problem a design challenge that requires creative solutions and innovation?\\n31. Does the problem require addressing systemic or structural issues rather than just individual instances?\\n32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\\n33. What kinds of solution typically are produced for this kind of problem specification?\\n34. Given the problem specification and the current best solution, have a guess about other possible solutions.35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?37. Ignoring the current best solution, create an entirely new solution to the problem.39. Let’s make a step by step plan and implement it with good notation and explanation.',\n",
-       " 'selected_modules': 'To solve the task of identifying the shape drawn by the given SVG path element, the following reasoning modules are crucial:\\n\\n1. **Critical Thinking (10)**: This involves analyzing the SVG path commands and coordinates logically to understand the shape they form. It requires questioning assumptions (e.g., not assuming the shape based on a quick glance at the coordinates but rather analyzing the path commands and their implications) and evaluating the information provided by the SVG path data.\\n\\n2. **Analytical Problem Solving (29)**: The task requires data analysis skills to interpret the SVG path commands and coordinates. Understanding how the \"M\" (moveto) and \"L\" (lineto) commands work to draw lines between specified points is essential for determining the shape.\\n\\n3. **Creative Thinking (11)**: While the task primarily involves analytical skills, creative thinking can help in visualizing the shape that the path commands are likely to form, especially when the path data doesn\\'t immediately suggest a common shape.\\n\\n4. **Systems Thinking (13)**: Recognizing the SVG path as part of a larger system (in this case, the SVG graphics system) and understanding how individual path commands contribute to the overall shape can be helpful. This involves understanding the interconnectedness of the start and end points of each line segment and how they come together to form a complete shape.\\n\\n5. **Break Down the Problem (9)**: Breaking down the SVG path into its individual commands and analyzing each segment between \"M\" and \"L\" commands can simplify the task. This makes it easier to visualize and understand the shape being drawn step by step.\\n\\n6. **Visualization (not explicitly listed but implied in creative and analytical thinking)**: Visualizing the path that the \"M\" and \"L\" commands create is essential. This isn\\'t a listed module but is a skill that underpins both creative and analytical approaches to solving this problem.\\n\\nGiven the SVG path commands, one would analyze each segment drawn by \"M\" (moveto) and \"L\" (lineto) commands to determine the shape\\'s vertices and sides. This process involves critical thinking to assess the information, analytical skills to interpret the path data, and a degree of creative thinking for visualization. The task does not directly involve assessing risks, long-term implications, or stakeholder perspectives, so modules focused on those aspects (e.g., Risk Analysis (14), Long-term Implications (8)) are less relevant here.',\n",
-       " 'adapted_modules': 'To enhance the process of identifying the shape drawn by the given SVG path element, the reasoning modules can be adapted and specified as follows:\\n\\n1. **Detailed Path Analysis (Critical Thinking)**: This module focuses on a meticulous examination of the SVG path commands and coordinates. It involves a deep dive into the syntax and semantics of path commands such as \"M\" (moveto) and \"L\" (lineto), challenging initial perceptions and rigorously interpreting the sequence of commands to deduce the shape accurately. This analysis goes beyond surface-level inspection, requiring a systematic questioning of each command\\'s role in constructing the overall shape.\\n\\n2. **Path Command Interpretation (Analytical Problem Solving)**: Essential for this task is the ability to decode the SVG path\\'s \"M\" and \"L\" commands, translating these instructions into a mental or visual representation of the shape\\'s geometry. This module emphasizes the analytical dissection of the path data, focusing on how each command contributes to the formation of vertices and edges, thereby facilitating the identification of the shape.\\n\\n3. **Shape Visualization (Creative Thinking)**: Leveraging imagination to mentally construct the shape from the path commands is the core of this module. It involves creatively synthesizing the segments drawn by the \"M\" and \"L\" commands into a coherent visual image, even when the path data does not immediately suggest a recognizable shape. This creative process aids in bridging gaps in the analytical interpretation, offering alternative perspectives on the possible shape outcomes.\\n\\n4. **Path-to-Shape Synthesis (Systems Thinking)**: This module entails understanding the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape. It requires an appreciation of the cumulative effect of each command in relation to the others, recognizing the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\\n\\n5. **Sequential Command Analysis (Break Down the Problem)**: By segmenting the SVG path into discrete commands, this approach simplifies the complexity of the task. It advocates for a step-by-step examination of the path, where each \"M\" to \"L\" sequence is analyzed in isolation before synthesizing the findings to understand the overall shape. This methodical breakdown facilitates a clearer visualization and comprehension of the shape being drawn.\\n\\n6. **Command-to-Geometry Mapping (Visualization)**: Central to solving this task is the ability to map the abstract \"M\" and \"L\" commands onto a concrete geometric representation. This implicit module underlies both the analytical and creative thinking processes, focusing on converting the path data into a visual form that can be easily understood and manipulated mentally. It is about constructing a mental image of the shape as each command is processed, enabling a dynamic visualization that evolves with each new piece of path data.\\n\\nBy adapting and specifying these reasoning modules, the task of identifying the shape drawn by the SVG path element becomes a structured process that leverages critical analysis, analytical problem-solving, creative visualization, systemic thinking, and methodical breakdown to accurately determine the shape as a (D) kite.',\n",
-       " 'reasoning_structure': '```json\\n{\\n  \"Step 1: Detailed Path Analysis\": {\\n    \"Description\": \"Examine each SVG path command and its coordinates closely. Understand the syntax and semantics of \\'M\\' (moveto) and \\'L\\' (lineto) commands.\",\\n    \"Action\": \"List all path commands and their coordinates.\",\\n    \"Expected Outcome\": \"A clear understanding of the sequence and direction of each path command.\"\\n  },\\n  \"Step 2: Path Command Interpretation\": {\\n    \"Description\": \"Decode the \\'M\\' and \\'L\\' commands to translate these instructions into a mental or visual representation of the shape\\'s geometry.\",\\n    \"Action\": \"Map each \\'M\\' and \\'L\\' command to its corresponding action (move or draw line) in the context of the shape.\",\\n    \"Expected Outcome\": \"A segmented representation of the shape, highlighting vertices and edges.\"\\n  },\\n  \"Step 3: Shape Visualization\": {\\n    \"Description\": \"Use imagination to mentally construct the shape from the path commands, synthesizing the segments into a coherent visual image.\",\\n    \"Action\": \"Visualize the shape based on the segmented representation from Step 2.\",\\n    \"Expected Outcome\": \"A mental image of the potential shape, considering the sequence and direction of path commands.\"\\n  },\\n  \"Step 4: Path-to-Shape Synthesis\": {\\n    \"Description\": \"Understand the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape.\",\\n    \"Action\": \"Analyze the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\",\\n    \"Expected Outcome\": \"Identification of the overall shape by recognizing the cumulative effect of each command.\"\\n  },\\n  \"Step 5: Sequential Command Analysis\": {\\n    \"Description\": \"Segment the SVG path into discrete commands for a step-by-step examination, analyzing each \\'M\\' to \\'L\\' sequence in isolation.\",\\n    \"Action\": \"Break down the path into individual commands and analyze each separately before synthesizing the findings.\",\\n    \"Expected Outcome\": \"A clearer visualization and comprehension of the shape being drawn, segment by segment.\"\\n  },\\n  \"Step 6: Command-to-Geometry Mapping\": {\\n    \"Description\": \"Map the abstract \\'M\\' and \\'L\\' commands onto a concrete geometric representation, constructing a mental image of the shape as each command is processed.\",\\n    \"Action\": \"Convert the path data into a visual form that can be easily understood and manipulated mentally.\",\\n    \"Expected Outcome\": \"A dynamic visualization of the shape that evolves with each new piece of path data, leading to the identification of the shape as a kite.\"\\n  },\\n  \"Conclusion\": {\\n    \"Description\": \"Based on the analysis and visualization steps, determine the shape drawn by the SVG path element.\",\\n    \"Action\": \"Review the outcomes of each step and synthesize the information to identify the shape.\",\\n    \"Expected Outcome\": \"The correct identification of the shape, supported by the structured analysis and reasoning process.\"\\n  }\\n}\\n```',\n",
-       " 'answer': 'Based on the provided reasoning structure and the SVG path element given, let\\'s analyze the path commands to identify the shape.\\n\\n**Step 1: Detailed Path Analysis**\\n- Description: The SVG path provided contains multiple \\'M\\' (moveto) and \\'L\\' (lineto) commands. Each command specifies a point in a 2D coordinate system.\\n- Action: The path commands are as follows:\\n  1. M 55.57,80.69 (Move to point)\\n  2. L 57.38,65.80 (Line to point)\\n  3. M 57.38,65.80 (Move to point)\\n  4. L 48.90,57.46 (Line to point)\\n  5. M 48.90,57.46 (Move to point)\\n  6. L 45.58,47.78 (Line to point)\\n  7. M 45.58,47.78 (Move to point)\\n  8. L 53.25,36.07 (Line to point)\\n  9. L 66.29,48.90 (Line to point)\\n  10. L 78.69,61.09 (Line to point)\\n  11. L 55.57,80.69 (Line to point)\\n- Expected Outcome: Understanding that the path commands describe a series of movements and lines that form a closed shape.\\n\\n**Step 2: Path Command Interpretation**\\n- Description: The \\'M\\' and \\'L\\' commands are used to move the \"pen\" to a starting point and draw lines to subsequent points, respectively.\\n- Action: The commands describe a shape starting at (55.57,80.69), drawing lines through several points, and finally closing the shape by returning to the starting point.\\n- Expected Outcome: A segmented representation showing a shape with distinct vertices at the specified coordinates.\\n\\n**Step 3: Shape Visualization**\\n- Description: Mentally constructing the shape from the provided path commands.\\n- Action: Visualizing the lines connecting in sequence from the starting point, through each point described by the \\'L\\' commands, and back to the starting point.\\n- Expected Outcome: A mental image of a shape that appears to have four distinct sides, suggesting it could be a quadrilateral.\\n\\n**Step 4: Path-to-Shape Synthesis**\\n- Description: Understanding how the path commands collectively form a specific shape.\\n- Action: Recognizing that the shape starts and ends at the same point, with lines drawn between intermediate points without overlapping, except at the starting/ending point.\\n- Expected Outcome: Identification of a closed, four-sided figure, which suggests it could be a kite based on the symmetry and structure of the lines.\\n\\n**Step 5: Sequential Command Analysis**\\n- Description: Analyzing each \\'M\\' to \\'L\\' sequence in isolation.\\n- Action: Observing that the path does not describe a regular polygon (like a hexagon or octagon) or a circle, but rather a shape with distinct angles and sides.\\n- Expected Outcome: A clearer understanding that the shape has four sides, with two pairs of adjacent sides being potentially unequal, which is characteristic of a kite.\\n\\n**Step 6: Command-to-Geometry Mapping**\\n- Description: Converting the abstract path commands into a geometric shape.\\n- Action: Mapping the path data to visualize a shape with two pairs of adjacent sides that are distinct yet symmetrical, indicative of a kite.\\n- Expected Outcome: A dynamic visualization that evolves to clearly represent a kite shape.\\n\\n**Conclusion**\\n- Description: Determining the shape drawn by the SVG path element.\\n- Action: Reviewing the outcomes of each analysis step, which consistently point towards a four-sided figure with distinct properties of a kite.\\n- Expected Outcome: The correct identification of the shape as a kite (D).'}"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "overall_chain.invoke(\n",
-    "    {\"task_description\": task_example, \"reasoning_modules\": reasoning_modules_str}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ea8568d5-bdb6-45cd-8d04-1ab305786caa",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c14a291c-7c1b-43bc-807e-11180290985e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/sql_db_qa.mdx

@@ -670,6 +670,8 @@ local_llm = HuggingFacePipeline(pipeline=pipe)
 <CodeOutputBlock lang="python">
 
 ```
+    /workspace/langchain/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+      from .autonotebook import tqdm as notebook_tqdm
     Loading checkpoint shards: 100%|██████████| 8/8 [00:32<00:00,  4.11s/it]
 ```
 

cookbook/together_ai.ipynb

@@ -82,7 +82,7 @@
     "prompt = ChatPromptTemplate.from_template(template)\n",
     "\n",
     "# LLM\n",
-    "from langchain_together import Together\n",
+    "from langchain_community.llms import Together\n",
     "\n",
     "llm = Together(\n",
     "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",

docker/Makefile

@@ -1,12 +0,0 @@
-# Makefile
-
-build_graphdb:
-	docker build --tag graphdb ./graphdb
-
-start_graphdb:
-	docker-compose up -d graphdb
-
-down:
-	docker-compose down -v --remove-orphans
-
-.PHONY: build_graphdb start_graphdb down

docker/docker-compose.yml

@@ -1,21 +0,0 @@
-# docker-compose to make it easier to spin up integration tests.
-# Services should use NON standard ports to avoid collision with
-version: "3"
-name: langchain-tests
-
-services:
-  redis:
-    image: redis/redis-stack-server:latest
-    # We use non standard ports since 
-    # these instances are used for testing
-    # and users may already have existing
-    # redis instances set up locally
-    # for other projects
-    ports:
-      - "6020:6379"
-    volumes:
-      - ./redis-volume:/data
-  graphdb:
-    image: graphdb
-    ports:
-      - "6021:7200"

docker/graphdb/Dockerfile

@@ -1,5 +0,0 @@
-FROM ontotext/graphdb:10.5.1
-RUN mkdir -p /opt/graphdb/dist/data/repositories/langchain
-COPY config.ttl /opt/graphdb/dist/data/repositories/langchain/
-COPY graphdb_create.sh /run.sh
-ENTRYPOINT bash /run.sh

docker/graphdb/config.ttl

@@ -1,46 +0,0 @@
-@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
-@prefix rep: <http://www.openrdf.org/config/repository#>.
-@prefix sr: <http://www.openrdf.org/config/repository/sail#>.
-@prefix sail: <http://www.openrdf.org/config/sail#>.
-@prefix graphdb: <http://www.ontotext.com/config/graphdb#>.
-
-[] a rep:Repository ;
-    rep:repositoryID "langchain" ;
-    rdfs:label "" ;
-    rep:repositoryImpl [
-        rep:repositoryType "graphdb:SailRepository" ;
-        sr:sailImpl [
-            sail:sailType "graphdb:Sail" ;
-
-            graphdb:read-only "false" ;
-
-            # Inference and Validation
-            graphdb:ruleset "empty" ;
-            graphdb:disable-sameAs "true" ;
-            graphdb:check-for-inconsistencies "false" ;
-
-            # Indexing
-            graphdb:entity-id-size "32" ;
-            graphdb:enable-context-index "false" ;
-            graphdb:enablePredicateList "true" ;
-            graphdb:enable-fts-index "false" ;
-            graphdb:fts-indexes ("default" "iri") ;
-            graphdb:fts-string-literals-index "default" ;
-            graphdb:fts-iris-index "none" ;
-
-            # Queries and Updates
-            graphdb:query-timeout "0" ;
-            graphdb:throw-QueryEvaluationException-on-timeout "false" ;
-            graphdb:query-limit-results "0" ;
-
-            # Settable in the file but otherwise hidden in the UI and in the RDF4J console
-            graphdb:base-URL "http://example.org/owlim#" ;
-            graphdb:defaultNS "" ;
-            graphdb:imports "" ;
-            graphdb:repository-type "file-repository" ;
-            graphdb:storage-folder "storage" ;
-            graphdb:entity-index-size "10000000" ;
-            graphdb:in-memory-literal-properties "true" ;
-            graphdb:enable-literal-index "true" ;
-        ]
-    ].

docker/graphdb/graphdb_create.sh

@@ -1,28 +0,0 @@
-#! /bin/bash
-REPOSITORY_ID="langchain"
-GRAPHDB_URI="http://localhost:7200/"
-
-echo -e "\nUsing GraphDB: ${GRAPHDB_URI}"
-
-function startGraphDB {
- echo -e "\nStarting GraphDB..."
- exec /opt/graphdb/dist/bin/graphdb
-}
-
-function waitGraphDBStart {
-  echo -e "\nWaiting GraphDB to start..."
-  for _ in $(seq 1 5); do
-    CHECK_RES=$(curl --silent --write-out '%{http_code}' --output /dev/null ${GRAPHDB_URI}/rest/repositories)
-    if [ "${CHECK_RES}" = '200' ]; then
-        echo -e "\nUp and running"
-        break
-    fi
-    sleep 30s
-    echo "CHECK_RES: ${CHECK_RES}"
-  done
-}
-
-
-startGraphDB &
-waitGraphDBStart
-wait

docs/.local_build.sh

@@ -16,8 +16,7 @@ cp ../cookbook/README.md src/pages/cookbook.mdx
 mkdir -p docs/templates
 cp ../templates/docs/INDEX.md docs/templates/index.md
 poetry run python scripts/copy_templates.py
-wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
-wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O docs/langgraph.md
+wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
 
 yarn
 

docs/api_reference/conf.py

@@ -146,7 +146,6 @@ partners = [
     (p.name, p.name.replace("-", "_") + "_api_reference")
     for p in partners_dir.iterdir()
 ]
-partners = sorted(partners)
 
 html_context = {
     "display_github": True,  # Integrate GitHub

docs/api_reference/create_api_rst.py

@@ -1,5 +1,4 @@
 """Script for auto-generating api_reference.rst."""
-
 import importlib
 import inspect
 import os
@@ -14,6 +13,7 @@ from pydantic import BaseModel
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
+
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
@@ -186,7 +186,7 @@ def _load_package_modules(
             modules_by_namespace[top_namespace] = _module_members
 
         except ImportError as e:
-            print(f"Error: Unable to import module '{namespace}' with error: {e}")  # noqa: T201
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")
 
     return modules_by_namespace
 
@@ -322,52 +322,30 @@ def _package_dir(package_name: str = "langchain") -> Path:
 
 
 def _get_package_version(package_dir: Path) -> str:
-    """Return the version of the package."""
-    try:
-        with open(package_dir.parent / "pyproject.toml", "r") as f:
-            pyproject = toml.load(f)
-    except FileNotFoundError as e:
-        print(
-            f"pyproject.toml not found in {package_dir.parent}.\n"
-            "You are either attempting to build a directory which is not a package or "
-            "the package is missing a pyproject.toml file which should be added."
-            "Aborting the build."
-        )
-        exit(1)
+    with open(package_dir.parent / "pyproject.toml", "r") as f:
+        pyproject = toml.load(f)
     return pyproject["tool"]["poetry"]["version"]
 
 
-def _out_file_path(package_name: str) -> Path:
+def _out_file_path(package_name: str = "langchain") -> Path:
     """Return the path to the file containing the documentation."""
     return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
-def _doc_first_line(package_name: str) -> str:
+def _doc_first_line(package_name: str = "langchain") -> str:
     """Return the path to the file containing the documentation."""
     return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
 
 
 def main() -> None:
     """Generate the api_reference.rst file for each package."""
-    print("Starting to build API reference files.")
     for dir in os.listdir(ROOT_DIR / "libs"):
-        # Skip any hidden directories
-        # Some of these could be present by mistake in the code base
-        # e.g., .pytest_cache from running tests from the wrong location.
-        if not dir.startswith("."):
-            print("Skipping dir:", dir)
-            continue
-
         if dir in ("cli", "partners"):
             continue
         else:
-            print("Building package:", dir)
             _build_rst_file(package_name=dir)
-    partner_packages = os.listdir(ROOT_DIR / "libs" / "partners")
-    print("Building partner packages:", partner_packages)
-    for dir in partner_packages:
+    for dir in os.listdir(ROOT_DIR / "libs" / "partners"):
         _build_rst_file(package_name=dir)
-    print("API reference files built.")
 
 
 if __name__ == "__main__":

docs/docs/_templates/integration.mdx

@@ -37,7 +37,7 @@ from langchain_community.llms import integration_class_REPLACE_ME
 
 ## Text Embedding Models
 
-See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME).
+See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME)
 
 ```python
 from langchain_community.embeddings import integration_class_REPLACE_ME
@@ -45,7 +45,7 @@ from langchain_community.embeddings import integration_class_REPLACE_ME
 
 ## Chat models
 
-See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME).
+See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME)
 
 ```python
 from langchain_community.chat_models import integration_class_REPLACE_ME

docs/docs/additional_resources/tutorials.mdx

@@ -2,7 +2,7 @@
 
 Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
 
-⛓ icon marks a new addition [last update 2024-02-06]
+⛓ icon marks a new addition [last update 2023-09-21]
 
 ---------------------
 
@@ -10,20 +10,18 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 ### Books
 
-#### [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
+#### ⛓[Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
 
 
 ### DeepLearning.AI courses
  by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
 - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
 - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
-- [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
+- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
 
 ### Handbook
 [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
 
-⛓ [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
-
 ### Short Tutorials
 [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 
@@ -31,8 +29,6 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
 
-⛓ [LangChain 101 Course](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb) by **Ivan Reznikov**
-
 ##  Tutorials
 
 ### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)
@@ -48,8 +44,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
 - [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo)
 - [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI)
-- [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr)
-- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ)
+- ⛓ [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr)
+- ⛓ [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ)
 
 
 ### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
@@ -113,16 +109,16 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs)
 - [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE)
 - [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k)
-- [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC)
-- [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4)
-- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN)
-- [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ)
-- [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9)
-- [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG)
-- [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe)
-- [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ)
-- [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI)
-- [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80)
+- ⛓ [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC)
+- ⛓ [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4)
+- ⛓ [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN)
+- ⛓ [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ)
+- ⛓ [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9)
+- ⛓ [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG)
+- ⛓ [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe)
+- ⛓ [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ)
+- ⛓ [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI)
+- ⛓ [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80)
 
 
 ### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
@@ -135,8 +131,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
 - [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
 - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) 
-- [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl)
-- [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB)
+- ⛓ [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl)
+- ⛓ [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB)
 
 
 ### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
@@ -152,4 +148,4 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 
 ---------------------
-⛓ icon marks a new addition [last update 2024-02-061]
+⛓ icon marks a new addition [last update 2023-09-21]

docs/docs/additional_resources/youtube.mdx

@@ -120,8 +120,6 @@
 - ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io)
 - ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV)
 - ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
-- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
-- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
 
 
 ### [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)
@@ -134,4 +132,4 @@
 
 
 ---------------------
-⛓ icon marks a new addition [last update 2024-02-04]
+⛓ icon marks a new addition [last update 2023-09-21]

docs/docs/contributing/documentation.mdx

@@ -17,7 +17,7 @@ Similar to linting, we recognize documentation can be annoying. If you do not wa
 ### Install dependencies
 
 - [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus.
-- `poetry install --with lint,docs --no-root` from the monorepo root.
+- `poetry install` from the monorepo root
 
 ### Building
 
@@ -49,6 +49,7 @@ make api_docs_linkcheck
 The docs are linted from the monorepo root. To lint the docs, run the following from there:
 
 ```bash
+poetry install --with lint,typing
 make lint
 ```
 

docs/docs/contributing/index.mdx

@@ -15,9 +15,8 @@ There are many ways to contribute to LangChain. Here are some common ways people
 - [**Documentation**](./documentation.mdx): Help improve our docs, including this one!
 - [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
-- [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
 
-### 🚩 GitHub Issues
+### 🚩GitHub Issues
 
 Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
 
@@ -32,13 +31,7 @@ We will try to keep these issues as up-to-date as possible, though
 with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
-### 💭 GitHub Discussions
-
-We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
-
-If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
-
-### 🙋 Getting Help
+### 🙋Getting Help
 
 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

docs/docs/contributing/repo_structure.mdx

@@ -1,54 +0,0 @@
----
-sidebar_position: 0.5
----
-# Repository Structure
-
-If you plan on contributing to LangChain code or documentation, it can be useful
-to understand the high level structure of the repository.
-
-LangChain is organized as a [monorep](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
-
-Here's the structure visualized as a tree:
-
-```text
-.
-├── cookbook # Tutorials and examples
-├── docs # Contains content for the documentation here: https://python.langchain.com/
-├── libs
-│   ├── langchain # Main package
-│   │   ├── tests/unit_tests # Unit tests (present in each package not shown for brevity)
-│   │   ├── tests/integration_tests # Integration tests (present in each package not shown for brevity)
-│   ├── langchain-community # Third-party integrations
-│   ├── langchain-core # Base interfaces for key abstractions
-│   ├── langchain-experimental # Experimental components and chains
-│   ├── partners
-│       ├── langchain-partner-1
-│       ├── langchain-partner-2
-│       ├── ...
-│
-├── templates # A collection of easily deployable reference architectures for a wide variety of tasks.
-```
-
-The root directory also contains the following files:
-
-* `pyproject.toml`: Dependencies for building docs and linting docs, cookbook.
-* `Makefile`: A file that contains shortcuts for building, linting and docs and cookbook.
-
-There are other files in the root directory level, but their presence should be self-explanatory. Feel free to browse around!
-
-## Documentation
-
-The `/docs` directory contains the content for the documentation that is shown
-at https://python.langchain.com/ and the associated API Reference https://api.python.langchain.com/en/latest/langchain_api_reference.html.
-
-See the [documentation](./documentation) guidelines to learn how to contribute to the documentation.
-
-## Code
-
-The `/libs` directory contains the code for the LangChain packages.
-
-To learn more about how to contribute code see the following guidelines:
-
-- [Code](./code.mdx) Learn how to develop in the LangChain codebase.
-- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package.
-- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages.

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -302,7 +302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/inspect.ipynb

@@ -85,10 +85,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "2448b6c2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(nodes={'7308e6063c6d40818c5a0cc1cc7444f2': Node(id='7308e6063c6d40818c5a0cc1cc7444f2', data=<class 'pydantic.main.RunnableParallel<context,question>Input'>), '292bbd8021d44ec3a31fbe724d9002c1': Node(id='292bbd8021d44ec3a31fbe724d9002c1', data=<class 'pydantic.main.RunnableParallel<context,question>Output'>), '9212f219cf05488f95229c56ea02b192': Node(id='9212f219cf05488f95229c56ea02b192', data=VectorStoreRetriever(tags=['FAISS', 'OpenAIEmbeddings'], vectorstore=<langchain_community.vectorstores.faiss.FAISS object at 0x117334f70>)), 'c7a8e65fa5cf44b99dbe7d1d6e36886f': Node(id='c7a8e65fa5cf44b99dbe7d1d6e36886f', data=RunnablePassthrough()), '818b9bfd40a341008373d5b9f9d0784b': Node(id='818b9bfd40a341008373d5b9f9d0784b', data=ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template='Answer the question based only on the following context:\\n{context}\\n\\nQuestion: {question}\\n'))])), 'b9f1d3ddfa6b4334a16ea439df22b11e': Node(id='b9f1d3ddfa6b4334a16ea439df22b11e', data=ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, openai_api_key='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', openai_proxy='')), '2bf84f6355c44731848345ca7d0f8ab9': Node(id='2bf84f6355c44731848345ca7d0f8ab9', data=StrOutputParser()), '1aeb2da5da5a43bb8771d3f338a473a2': Node(id='1aeb2da5da5a43bb8771d3f338a473a2', data=<class 'pydantic.main.StrOutputParserOutput'>)}, edges=[Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='9212f219cf05488f95229c56ea02b192'), Edge(source='9212f219cf05488f95229c56ea02b192', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='c7a8e65fa5cf44b99dbe7d1d6e36886f'), Edge(source='c7a8e65fa5cf44b99dbe7d1d6e36886f', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='292bbd8021d44ec3a31fbe724d9002c1', target='818b9bfd40a341008373d5b9f9d0784b'), Edge(source='818b9bfd40a341008373d5b9f9d0784b', target='b9f1d3ddfa6b4334a16ea439df22b11e'), Edge(source='2bf84f6355c44731848345ca7d0f8ab9', target='1aeb2da5da5a43bb8771d3f338a473a2'), Edge(source='b9f1d3ddfa6b4334a16ea439df22b11e', target='2bf84f6355c44731848345ca7d0f8ab9')])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "chain.get_graph()"
    ]

docs/docs/expression_language/how_to/message_history.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Add message history (memory)\n",
     "\n",
-    "The `RunnableWithMessageHistory` lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it.\n",
+    "The `RunnableWithMessageHistory` let's us add message history to certain types of chains.\n",
     "\n",
     "Specifically, it can be used for any Runnable that takes as input one of\n",
     "\n",
@@ -21,379 +21,7 @@
     "* a sequence of `BaseMessage`\n",
     "* a dict with a key that contains a sequence of `BaseMessage`\n",
     "\n",
-    "Let's take a look at some examples to see how it works. First we construct a runnable (which here accepts a dict as input and returns a message as output):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2ed413b4-33a1-48ee-89b0-2d4917ec101a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_openai.chat_models import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI()\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You're an assistant who's good at {ability}. Respond in 20 words or fewer\",\n",
-    "        ),\n",
-    "        MessagesPlaceholder(variable_name=\"history\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "runnable = prompt | model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fd175e1-c7b8-4929-a57e-3331865fe7aa",
-   "metadata": {},
-   "source": [
-    "To manage the message history, we will need:\n",
-    "1. This runnable;\n",
-    "2. A callable that returns an instance of `BaseChatMessageHistory`.\n",
-    "\n",
-    "Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using Redis and other providers. Here we demonstrate using an in-memory `ChatMessageHistory` as well as more persistent storage using `RedisChatMessageHistory`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d83adad-9672-496d-9f25-5747e7b8c8bb",
-   "metadata": {},
-   "source": [
-    "## In-memory\n",
-    "\n",
-    "Below we show a simple example in which the chat history lives in memory, in this case via a global Python dict.\n",
-    "\n",
-    "We construct a callable `get_session_history` that references this dict to return an instance of `ChatMessageHistory`. The arguments to the callable can be specified by passing a configuration to the `RunnableWithMessageHistory` at runtime. By default, the configuration parameter is expected to be a single string `session_id`. This can be adjusted via the `history_factory_config` kwarg.\n",
-    "\n",
-    "Using the single-parameter default:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "54348d02-d8ee-440c-bbf9-41bc0fbbc46c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "\n",
-    "store = {}\n",
-    "\n",
-    "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = ChatMessageHistory()\n",
-    "    return store[session_id]\n",
-    "\n",
-    "\n",
-    "with_message_history = RunnableWithMessageHistory(\n",
-    "    runnable,\n",
-    "    get_session_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"history\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01acb505-3fd3-4ab4-9f04-5ea07e81542e",
-   "metadata": {},
-   "source": [
-    "Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n",
-    "\n",
-    "When invoking this new runnable, we specify the corresponding chat history via a configuration parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "01384412-f08e-4634-9edb-3f46f475b582",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Cosine is a trigonometric function that calculates the ratio of the adjacent side to the hypotenuse of a right triangle.')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "954688a2-9a3f-47ee-a9e8-fa0c83e69477",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Cosine is a mathematical function used to calculate the length of a side in a right triangle.')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Remembers\n",
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "39350d7c-2641-4744-bc2a-fd6a57c4ea90",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='I can help with math problems. What do you need assistance with?')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# New session_id --> does not remember.\n",
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"def234\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d29497be-3366-408d-bbb9-d4a8bf4ef37c",
-   "metadata": {},
-   "source": [
-    "The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1c89daee-deff-4fdf-86a3-178f7d8ef536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.runnables import ConfigurableFieldSpec\n",
-    "\n",
-    "store = {}\n",
-    "\n",
-    "\n",
-    "def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:\n",
-    "    if (user_id, conversation_id) not in store:\n",
-    "        store[(user_id, conversation_id)] = ChatMessageHistory()\n",
-    "    return store[(user_id, conversation_id)]\n",
-    "\n",
-    "\n",
-    "with_message_history = RunnableWithMessageHistory(\n",
-    "    runnable,\n",
-    "    get_session_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"history\",\n",
-    "    history_factory_config=[\n",
-    "        ConfigurableFieldSpec(\n",
-    "            id=\"user_id\",\n",
-    "            annotation=str,\n",
-    "            name=\"User ID\",\n",
-    "            description=\"Unique identifier for the user.\",\n",
-    "            default=\"\",\n",
-    "            is_shared=True,\n",
-    "        ),\n",
-    "        ConfigurableFieldSpec(\n",
-    "            id=\"conversation_id\",\n",
-    "            annotation=str,\n",
-    "            name=\"Conversation ID\",\n",
-    "            description=\"Unique identifier for the conversation.\",\n",
-    "            default=\"\",\n",
-    "            is_shared=True,\n",
-    "        ),\n",
-    "    ],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65c5622e-09b8-4f2f-8c8a-2dab0fd040fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"Hello\"},\n",
-    "    config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18f1a459-3f88-4ee6-8542-76a907070dd6",
-   "metadata": {},
-   "source": [
-    "### Examples with runnables of different signatures\n",
-    "\n",
-    "The above runnable takes a dict as input and returns a BaseMessage. Below we show some alternatives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48eae1bf-b59d-4a61-8e62-b6dbf667e866",
-   "metadata": {},
-   "source": [
-    "#### Messages input, dict output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "17733d4f-3a32-4055-9d44-5d58b9446a26",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content=\"Simone de Beauvoir believed in the existence of free will. She argued that individuals have the ability to make choices and determine their own actions, even in the face of social and cultural constraints. She rejected the idea that individuals are purely products of their environment or predetermined by biology or destiny. Instead, she emphasized the importance of personal responsibility and the need for individuals to actively engage in creating their own lives and defining their own existence. De Beauvoir believed that freedom and agency come from recognizing one's own freedom and actively exercising it in the pursuit of personal and collective liberation.\")}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.runnables import RunnableParallel\n",
-    "\n",
-    "chain = RunnableParallel({\"output_message\": ChatOpenAI()})\n",
-    "\n",
-    "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = ChatMessageHistory()\n",
-    "    return store[session_id]\n",
-    "\n",
-    "\n",
-    "with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    get_session_history,\n",
-    "    output_messages_key=\"output_message\",\n",
-    ")\n",
-    "\n",
-    "with_message_history.invoke(\n",
-    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "efb57ef5-91f9-426b-84b9-b77f071a9dd7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content='Simone de Beauvoir\\'s views on free will were closely aligned with those of her contemporary and partner Jean-Paul Sartre. Both de Beauvoir and Sartre were existentialist philosophers who emphasized the importance of individual freedom and the rejection of determinism. They believed that human beings have the capacity to transcend their circumstances and create their own meaning and values.\\n\\nSartre, in his famous work \"Being and Nothingness,\" argued that human beings are condemned to be free, meaning that we are burdened with the responsibility of making choices and defining ourselves in a world that lacks inherent meaning. Like de Beauvoir, Sartre believed that individuals have the ability to exercise their freedom and make choices in the face of external and internal constraints.\\n\\nWhile there may be some nuanced differences in their philosophical writings, overall, de Beauvoir and Sartre shared a similar belief in the existence of free will and the importance of individual agency in shaping one\\'s own life.')}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with_message_history.invoke(\n",
-    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a39eac5f-a9d8-4729-be06-5e7faf0c424d",
-   "metadata": {},
-   "source": [
-    "#### Messages input, messages output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e45bcd95-e31f-4a9a-967a-78f96e8da881",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "RunnableWithMessageHistory(\n",
-    "    ChatOpenAI(),\n",
-    "    get_session_history,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04daa921-a2d1-40f9-8cd1-ae4e9a4163a7",
-   "metadata": {},
-   "source": [
-    "#### Dict with single key for all messages input, messages output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27157f15-9fb0-4167-9870-f4d7f234b3cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter\n",
-    "\n",
-    "RunnableWithMessageHistory(\n",
-    "    itemgetter(\"input_messages\") | ChatOpenAI(),\n",
-    "    get_session_history,\n",
-    "    input_messages_key=\"input_messages\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "418ca7af-9ed9-478c-8bca-cba0de2ca61e",
-   "metadata": {},
-   "source": [
-    "## Persistent storage"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76799a13-d99a-4c4f-91f2-db699e40b8df",
-   "metadata": {},
-   "source": [
-    "In many cases it is preferable to persist conversation histories. `RunnableWithMessageHistory` is agnostic as to how the `get_session_history` callable retrieves its chat message histories. See [here](https://github.com/langchain-ai/langserve/blob/main/examples/chat_with_persistence_and_user/server.py) for an example using a local filesystem. Below we demonstrate how one could use Redis. Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers."
+    "Let's take a look at some examples to see how it works."
    ]
   },
   {
@@ -401,9 +29,9 @@
    "id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd",
    "metadata": {},
    "source": [
-    "### Setup\n",
+    "## Setup\n",
     "\n",
-    "We'll need to install Redis if it's not installed already:"
+    "We'll use Redis to store our chat message histories and Anthropic's claude-2 model so we'll need to install the following dependencies:"
    ]
   },
   {
@@ -413,7 +41,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet redis"
+    "%pip install --upgrade --quiet  langchain redis anthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93776323-d6b8-4912-bb6a-867c5e655f46",
+   "metadata": {},
+   "source": [
+    "Set your [Anthropic API  key](https://console.anthropic.com/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7f56f69-d2f1-4a21-990c-b5551eb012fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
    ]
   },
   {
@@ -429,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68",
    "metadata": {},
    "outputs": [],
@@ -463,30 +112,75 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef",
+   "id": "1a5a632e-ba9e-4488-b586-640ad5494f62",
    "metadata": {},
    "source": [
-    "Updating the message history implementation just requires us to define a new callable, this time returning an instance of `RedisChatMessageHistory`:"
+    "## Example: Dict input, message output\n",
+    "\n",
+    "Let's create a simple chain that takes a dict as input and returns a BaseMessage.\n",
+    "\n",
+    "In this case the `\"question\"` key in the input represents our input message, and the `\"history\"` key is where our historical messages will be injected."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
+   "id": "2a150d6f-8878-4950-8634-a608c5faad56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional\n",
+    "\n",
+    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
+    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_core.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables.history import RunnableWithMessageHistory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3185edba-4eb6-4b32-80c6-577c0d19af97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're an assistant who's good at {ability}\"),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | ChatAnthropic(model=\"claude-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef",
+   "metadata": {},
+   "source": [
+    "### Adding message history\n",
+    "\n",
+    "To add message history to our original chain we wrap it in the `RunnableWithMessageHistory` class.\n",
+    "\n",
+    "Crucially, we also need to  define a method that takes a session_id string and based on it returns a `BaseChatMessageHistory`. Given the same input, this method should return an equivalent output.\n",
+    "\n",
+    "In this case we'll also want to specify `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "ca7c64d8-e138-4ef8-9734-f82076c47d80",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
-    "\n",
-    "\n",
-    "def get_message_history(session_id: str) -> RedisChatMessageHistory:\n",
-    "    return RedisChatMessageHistory(session_id, url=REDIS_URL)\n",
-    "\n",
-    "\n",
-    "with_message_history = RunnableWithMessageHistory(\n",
-    "    runnable,\n",
-    "    get_message_history,\n",
-    "    input_messages_key=\"input\",\n",
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    input_messages_key=\"question\",\n",
     "    history_messages_key=\"history\",\n",
     ")"
    ]
@@ -496,53 +190,60 @@
    "id": "37eefdec-9901-4650-b64c-d3c097ed5f4d",
    "metadata": {},
    "source": [
-    "We can invoke as before:"
+    "## Invoking with config\n",
+    "\n",
+    "Whenever we call our chain with message history, we need to include a config that contains the `session_id`\n",
+    "```python\n",
+    "config={\"configurable\": {\"session_id\": \"<SESSION_ID>\"}}\n",
+    "```\n",
+    "\n",
+    "Given the same configuration, our chain should be pulling from the same chat message history."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "a85bcc22-ca4c-4ad5-9440-f94be7318f3e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Cosine is a trigonometric function that represents the ratio of the adjacent side to the hypotenuse in a right triangle.')"
+       "AIMessage(content=' Cosine is one of the basic trigonometric functions in mathematics. It is defined as the ratio of the adjacent side to the hypotenuse in a right triangle.\\n\\nSome key properties and facts about cosine:\\n\\n- It is denoted by cos(θ), where θ is the angle in a right triangle. \\n\\n- The cosine of an acute angle is always positive. For angles greater than 90 degrees, cosine can be negative.\\n\\n- Cosine is one of the three main trig functions along with sine and tangent.\\n\\n- The cosine of 0 degrees is 1. As the angle increases towards 90 degrees, the cosine value decreases towards 0.\\n\\n- The range of values for cosine is -1 to 1.\\n\\n- The cosine function maps angles in a circle to the x-coordinate on the unit circle.\\n\\n- Cosine is used to find adjacent side lengths in right triangles, and has many other applications in mathematics, physics, engineering and more.\\n\\n- Key cosine identities include: cos(A+B) = cosAcosB − sinAsinB and cos(2A) = cos^2(A) − sin^2(A)\\n\\nSo in summary, cosine is a fundamental trig')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
+    "chain_with_history.invoke(\n",
+    "    {\"ability\": \"math\", \"question\": \"What does cosine mean?\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "ab29abd3-751f-41ce-a1b0-53f6b565e79d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='The inverse of cosine is the arccosine function, denoted as acos or cos^-1, which gives the angle corresponding to a given cosine value.')"
+       "AIMessage(content=' The inverse of the cosine function is called the arccosine or inverse cosine, often denoted as cos-1(x) or arccos(x).\\n\\nThe key properties and facts about arccosine:\\n\\n- It is defined as the angle θ between 0 and π radians whose cosine is x. So arccos(x) = θ such that cos(θ) = x.\\n\\n- The range of arccosine is 0 to π radians (0 to 180 degrees).\\n\\n- The domain of arccosine is -1 to 1. \\n\\n- arccos(cos(θ)) = θ for values of θ from 0 to π radians.\\n\\n- arccos(x) is the angle in a right triangle whose adjacent side is x and hypotenuse is 1.\\n\\n- arccos(0) = 90 degrees. As x increases from 0 to 1, arccos(x) decreases from 90 to 0 degrees.\\n\\n- arccos(1) = 0 degrees. arccos(-1) = 180 degrees.\\n\\n- The graph of y = arccos(x) is part of the unit circle, restricted to x')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "with_message_history.invoke(\n",
-    "    {\"ability\": \"math\", \"input\": \"What's its inverse\"},\n",
+    "chain_with_history.invoke(\n",
+    "    {\"ability\": \"math\", \"question\": \"What's its inverse\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
@@ -554,7 +255,7 @@
    "source": [
     ":::tip\n",
     "\n",
-    "[Langsmith trace](https://smith.langchain.com/public/bd73e122-6ec1-48b2-82df-e6483dc9cb63/r)\n",
+    "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
     "\n",
     ":::"
    ]
@@ -566,13 +267,124 @@
    "source": [
     "Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "028cf151-6cd5-4533-b3cf-c8d735554647",
+   "metadata": {},
+   "source": [
+    "## Example: messages input, dict output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0bb446b5-6251-45fe-a92a-4c6171473c53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=' Here is a summary of Simone de Beauvoir\\'s views on free will:\\n\\n- De Beauvoir was an existentialist philosopher and believed strongly in the concept of free will. She rejected the idea that human nature or instincts determine behavior.\\n\\n- Instead, de Beauvoir argued that human beings define their own essence or nature through their actions and choices. As she famously wrote, \"One is not born, but rather becomes, a woman.\"\\n\\n- De Beauvoir believed that while individuals are situated in certain cultural contexts and social conditions, they still have agency and the ability to transcend these situations. Freedom comes from choosing one\\'s attitude toward these constraints.\\n\\n- She emphasized the radical freedom and responsibility of the individual. We are \"condemned to be free\" because we cannot escape making choices and taking responsibility for our choices. \\n\\n- De Beauvoir felt that many people evade their freedom and responsibility by adopting rigid mindsets, ideologies, or conforming uncritically to social roles.\\n\\n- She advocated for the recognition of ambiguity in the human condition and warned against the quest for absolute rules that deny freedom and responsibility. Authentic living involves embracing ambiguity.\\n\\nIn summary, de Beauvoir promoted an existential ethics')}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.runnables import RunnableParallel\n",
+    "\n",
+    "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n",
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    output_messages_key=\"output_message\",\n",
+    ")\n",
+    "\n",
+    "chain_with_history.invoke(\n",
+    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "601ce3ff-aea8-424d-8e54-fd614256af4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=\" There are many similarities between Simone de Beauvoir's views on free will and those of Jean-Paul Sartre, though some key differences emerge as well:\\n\\nSimilarities with Sartre:\\n\\n- Both were existentialist thinkers who rejected determinism and emphasized human freedom and responsibility.\\n\\n- They agreed that existence precedes essence - there is no predefined human nature that determines who we are.\\n\\n- Individuals must define themselves through their choices and actions. This leads to anxiety but also freedom.\\n\\n- The human condition is characterized by ambiguity and uncertainty, rather than fixed meanings/values.\\n\\n- Both felt that most people evade their freedom through self-deception, conformity, or adopting collective identities/values uncritically.\\n\\nDifferences from Sartre: \\n\\n- Sartre placed more emphasis on the burden and anguish of radical freedom. De Beauvoir focused more on its positive potential.\\n\\n- De Beauvoir critiqued Sartre's premise that human relations are necessarily conflictual. She saw more potential for mutual recognition.\\n\\n- Sartre saw the Other's gaze as a threat to freedom. De Beauvoir put more stress on how the Other's gaze can confirm\")}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_with_history.invoke(\n",
+    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1724292c-01c6-44bb-83e8-9cdb6bf01483",
+   "metadata": {},
+   "source": [
+    "## More examples\n",
+    "\n",
+    "We could also do any of the below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd89240b-5a25-48f8-9568-5c1127f9ffad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "# messages in, messages out\n",
+    "RunnableWithMessageHistory(\n",
+    "    ChatAnthropic(model=\"claude-2\"),\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    ")\n",
+    "\n",
+    "# dict with single key for all messages in, messages out\n",
+    "RunnableWithMessageHistory(\n",
+    "    itemgetter(\"input_messages\") | ChatAnthropic(model=\"claude-2\"),\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    input_messages_key=\"input_messages\",\n",
+    ")"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -584,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/get_started/introduction.mdx

@@ -78,7 +78,7 @@ Let models choose which tools to use given high-level directives
 Walkthroughs and techniques for common end-to-end use cases, like:
 - [Document question answering](/docs/use_cases/question_answering/)
 - [Chatbots](/docs/use_cases/chatbots/)
-- [Analyzing structured data](/docs/use_cases/sql/)
+- [Analyzing structured data](/docs/use_cases/qa_structured/sql/)
 - and much more...
 
 ### [Integrations](/docs/integrations/providers/)
@@ -93,3 +93,6 @@ Head to the reference section for full documentation of all classes and methods
 ### [Developer's guide](/docs/contributing)
 Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
 
+### [Community](/docs/community)
+Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
+

docs/docs/get_started/quickstart.mdx

@@ -59,7 +59,7 @@ In this quickstart, we will walk through a few different ways of doing that.
 We will start with a simple LLM chain, which just relies on information in the prompt template to respond.
 Next, we will build a retrieval chain, which fetches data from a separate database and passes that into the prompt template.
 We will then add in chat history, to create a conversation retrieval chain. This allows you interact in a chat manner with this LLM, so it remembers previous questions.
-Finally, we will build an agent - which utilizes an LLM to determine whether or not it needs to fetch data to answer questions.
+Finally, we will build an agent - which utilizes and LLM to determine whether or not it needs to fetch data to answer questions.
 We will cover these at a high level, but there are lot of details to all of these!
 We will link to relevant docs.
 
@@ -184,6 +184,7 @@ A Retriever can be backed by anything - a SQL table, the internet, etc - but in
 
 First, we need to load the data that we want to index. In order to do this, we will use the WebBaseLoader. This requires installing [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/):
 
+```
 ```shell
 pip install beautifulsoup4
 ```
@@ -193,7 +194,7 @@ After that, we can import and use WebBaseLoader.
 
 ```python
 from langchain_community.document_loaders import WebBaseLoader
-loader = WebBaseLoader("https://docs.smith.langchain.com")
+loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
 
 docs = loader.load()
 ```
@@ -581,10 +582,7 @@ Using this, we can interact with the served chain as if it were running client-s
 from langserve import RemoteRunnable
 
 remote_chain = RemoteRunnable("http://localhost:8000/agent/")
-remote_chain.invoke({
-    "input": "how can langsmith help with testing?",
-    "chat_history": []  # Providing an empty list as this is the first call
-})
+remote_chain.invoke({"input": "how can langsmith help with testing?"})
 ```
 
 To learn more about the many other features of LangServe [head here](/docs/langserve).
@@ -599,6 +597,6 @@ To continue on your journey, we recommend you read the following (in order):
 - [Model IO](/docs/modules/model_io) covers more details of prompts, LLMs, and output parsers.
 - [Retrieval](/docs/modules/data_connection) covers more details of everything related to retrieval
 - [Agents](/docs/modules/agents) covers details of everything related to agents
-- Explore common [end-to-end use cases](/docs/use_cases/) and [template applications](/docs/templates)
+- Explore common [end-to-end use cases](/docs/use_cases/qa_structured/sql) and [template applications](/docs/templates)
 - [Read up on LangSmith](/docs/langsmith/), the platform for debugging, testing, monitoring and more
 - Learn more about serving your applications with [LangServe](/docs/langserve)

docs/docs/guides/deployments/index.mdx

@@ -98,7 +98,7 @@ The LLM landscape is evolving at an unprecedented pace, with new libraries and m
 
 ### 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 feedback 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.
+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
 

docs/docs/guides/model_laboratory.ipynb

@@ -35,22 +35,6 @@
     "from langchain_openai import OpenAI"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3dd69cb4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# get a new token: https://dashboard.cohere.ai/\n",
-    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Cohere API Key:\")\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Open API Key:\")\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\"Hugging Face API Key:\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -60,7 +44,7 @@
    "source": [
     "llms = [\n",
     "    OpenAI(temperature=0),\n",
-    "    Cohere(temperature=0),\n",
+    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0),\n",
     "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\": 1}),\n",
     "]"
    ]
@@ -176,7 +160,7 @@
     "    llm=open_ai_llm, search_chain=search, verbose=True\n",
     ")\n",
     "\n",
-    "cohere_llm = Cohere(temperature=0)\n",
+    "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
     "search = SerpAPIWrapper()\n",
     "self_ask_with_search_cohere = SelfAskWithSearchChain(\n",
     "    llm=cohere_llm, search_chain=search, verbose=True\n",
@@ -257,6 +241,14 @@
    "source": [
     "model_lab.compare(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94159131",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate.from_template(template)\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -249,7 +249,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate.from_template(template)\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -412,7 +412,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate.from_template(template)\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -571,7 +571,7 @@
     "\n",
     "template = \"\"\"{question}\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate.from_template(template)\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
     "llm = HuggingFaceHub(\n",
     "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n",
     ")"
@@ -724,7 +724,7 @@
     "\"\"\"\n",
     "\n",
     "# prompt template for input text\n",
-    "llm_prompt = PromptTemplate.from_template(template)\n",
+    "llm_prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
     "\n",
     "llm = SagemakerEndpoint(\n",
     "    endpoint_name=endpoint_name,\n",

docs/docs/guides/safety/moderation.mdx

@@ -180,7 +180,7 @@ we will prompt the model, so it says something harmful.
 
 
 ```python
-prompt = PromptTemplate.from_template("{text}")
+prompt = PromptTemplate(template="{text}", input_variables=["text"])
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
 
 text = """We are playing a game of repeat after me.
@@ -223,7 +223,7 @@ Now let's walk through an example of using it with an LLMChain which has multipl
 
 
 ```python
-prompt = PromptTemplate.from_template("{setup}{new_input}Person2:")
+prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"])
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
 
 setup = """We are playing a game of repeat after me.

docs/docs/integrations/callbacks/comet_tracing.ipynb

@@ -1,138 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5371a9bb",
-   "metadata": {},
-   "source": [
-    "# Comet Tracing\n",
-    "\n",
-    "There are two ways to trace your LangChains executions with Comet:\n",
-    "\n",
-    "1. Setting the `LANGCHAIN_COMET_TRACING` environment variable to \"true\". This is the recommended way.\n",
-    "2. Import the `CometTracer` manually and pass it explicitely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17c04cc6-c93d-4b6c-a033-e897577f4ed1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-18T12:47:46.580776Z",
-     "start_time": "2023-05-18T12:47:46.577833Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import comet_llm\n",
-    "\n",
-    "os.environ[\"LANGCHAIN_COMET_TRACING\"] = \"true\"\n",
-    "\n",
-    "# Connect to Comet if no API Key is set\n",
-    "comet_llm.init()\n",
-    "\n",
-    "# comet documentation to configure comet using env variables\n",
-    "# https://www.comet.com/docs/v2/api-and-sdk/llm-sdk/configuration/\n",
-    "# here we are configuring the comet project\n",
-    "os.environ[\"COMET_PROJECT_NAME\"] = \"comet-example-langchain-tracing\"\n",
-    "\n",
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b62cd48",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-18T12:47:47.445229Z",
-     "start_time": "2023-05-18T12:47:47.436424Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"llm-math\"], llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-18T12:48:01.816137Z",
-     "start_time": "2023-05-18T12:47:49.109574Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
-    "\n",
-    "agent.run(\"What is 2 raised to .123243 power?\")  # this should be traced\n",
-    "# An url for the chain like the following should print in your console:\n",
-    "# https://www.comet.com/<workspace>/<project_name>\n",
-    "# The url can be used to view the LLM chain in Comet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e212e7d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now, we unset the environment variable and use a context manager.\n",
-    "if \"LANGCHAIN_COMET_TRACING\" in os.environ:\n",
-    "    del os.environ[\"LANGCHAIN_COMET_TRACING\"]\n",
-    "\n",
-    "from langchain.callbacks.tracers.comet import CometTracer\n",
-    "\n",
-    "tracer = CometTracer()\n",
-    "\n",
-    "# Recreate the LLM, tools and agent and passing the callback to each of them\n",
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"llm-math\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
-    "\n",
-    "agent.run(\n",
-    "    \"What is 2 raised to .123243 power?\", callbacks=[tracer]\n",
-    ")  # this should be traced"
-   ]
-  }
- ],
- "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/callbacks/streamlit.md

@@ -28,7 +28,7 @@ You can run `streamlit hello` to load a sample app and validate your install suc
 To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output.
 
 ```python
-from langchain_community.callbacks import StreamlitCallbackHandler
+from langchain.callbacks import StreamlitCallbackHandler
 import streamlit as st
 
 st_callback = StreamlitCallbackHandler(st.container())
@@ -44,26 +44,23 @@ agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `a
 thoughts and actions live in your app.
 
 ```python
-import streamlit as st
-from langchain import hub
-from langchain.agents import AgentExecutor, create_react_agent, load_tools
-from langchain_community.callbacks import StreamlitCallbackHandler
 from langchain_openai import OpenAI
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
 
 llm = OpenAI(temperature=0, streaming=True)
 tools = load_tools(["ddg-search"])
-prompt = hub.pull("hwchase17/react")
-agent = create_react_agent(llm, tools, prompt)
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+agent = initialize_agent(
+    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+)
 
 if prompt := st.chat_input():
     st.chat_message("user").write(prompt)
     with st.chat_message("assistant"):
         st_callback = StreamlitCallbackHandler(st.container())
-        response = agent_executor.invoke(
-            {"input": prompt}, {"callbacks": [st_callback]}
-        )
-        st.write(response["output"])
+        response = agent.run(prompt, callbacks=[st_callback])
+        st.write(response)
 ```
 
 **Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully.

docs/docs/integrations/chat/ai21.ipynb

@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "4cebeec0",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AI21 Labs\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatAI21\n",
-    "\n",
-    "This notebook covers how to get started with AI21 chat models.\n",
-    "\n",
-    "## Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-02-15T06:50:44.929635Z",
-     "start_time": "2024-02-15T06:50:41.209704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install -qU langchain-ai21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Environment Setup\n",
-    "\n",
-    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4828829d3da430ce",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "39353473fce5dd2e",
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Bonjour, comment vas-tu?')"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_ai21 import ChatAI21\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "chat = ChatAI21(model=\"j2-ultra\")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
-    "        (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke({\"english_text\": \"Hello, how are you?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c159a79f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/anthropic.ipynb

@@ -22,88 +22,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:00.590587Z",
-     "start_time": "2024-01-19T11:25:00.127293Z"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatAnthropic\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_community.chat_models import ChatAnthropic"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:04.349676Z",
-     "start_time": "2024-01-19T11:25:03.964930Z"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1f9df276476f0bc",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "The code provided assumes that your ANTHROPIC_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:\n",
-    "```python\n",
-    "chat = ChatAnthropic(temperature=0, anthropic_api_key=\"YOUR_API_KEY\", model_name=\"claude-instant-1.2\")\n",
-    "\n",
-    "```\n",
-    "Please note that the default model is \"claude-2,\" and you can check the available models at [here](https://docs.anthropic.com/claude/reference/selecting-a-model)."
+    "chat = ChatAnthropic()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:07.274418Z",
-     "start_time": "2024-01-19T11:25:05.898031Z"
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content=' 저는 파이썬을 좋아합니다.')"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "system = (\n",
-    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
-    ")\n",
-    "human = \"{text}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"Korean\",\n",
-    "        \"text\": \"I love Python\",\n",
-    "    }\n",
-    ")"
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "    )\n",
+    "]\n",
+    "chat.invoke(messages)"
    ]
   },
   {
@@ -116,78 +72,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "execution_count": null,
+   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:10.448733Z",
-     "start_time": "2024-01-19T11:25:08.866277Z"
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content=\" Why don't bears like fast food? Because they can't catch it!\")"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Tell me a joke about {topic}\")])\n",
-    "chain = prompt | chat\n",
-    "await chain.ainvoke({\"topic\": \"bear\"})"
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "execution_count": null,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:24.438696Z",
-     "start_time": "2024-01-19T11:25:14.687480Z"
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Here are some of the most famous tourist attractions in Japan:\n",
-      "\n",
-      "- Tokyo - Tokyo Tower, Tokyo Skytree, Imperial Palace, Sensoji Temple, Meiji Shrine, Shibuya Crossing\n",
-      "\n",
-      "- Kyoto - Kinkakuji (Golden Pavilion), Fushimi Inari Shrine, Kiyomizu-dera Temple, Arashiyama Bamboo Grove, Gion Geisha District\n",
-      "\n",
-      "- Osaka - Osaka Castle, Dotonbori, Universal Studios Japan, Osaka Aquarium Kaiyukan \n",
-      "\n",
-      "- Hiroshima - Hiroshima Peace Memorial Park and Museum, Itsukushima Shrine (Miyajima Island)\n",
-      "\n",
-      "- Mount Fuji - Iconic and famous mountain, popular for hiking and viewing from places like Hakone and Kawaguchiko Lake\n",
-      "\n",
-      "- Himeji - Himeji Castle, one of Japan's most impressive feudal castles\n",
-      "\n",
-      "- Nara - Todaiji Temple, Nara Park with its bowing deer, Horyuji Temple with some of world's oldest wooden structures  \n",
-      "\n",
-      "- Nikko - Elaborate shrines and temples nestled around Nikko National Park\n",
-      "\n",
-      "- Sapporo - Snow"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "chat = ChatAnthropic(temperature=0.3, model_name=\"claude-2\")\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"human\", \"Give me a list of famous tourist attractions in Japan\")]\n",
+    "await chat.ainvoke([messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatAnthropic(\n",
+    "    streaming=True,\n",
+    "    verbose=True,\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
     ")\n",
-    "chain = prompt | chat\n",
-    "for chunk in chain.stream({}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
+    "chat.stream(messages)"
    ]
   },
   {
@@ -212,130 +134,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "07c47c2a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:25.288133Z",
-     "start_time": "2024-01-19T11:25:24.438968Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_anthropic import ChatAnthropicMessages\n",
     "\n",
     "chat = ChatAnthropicMessages(model_name=\"claude-instant-1.2\")\n",
-    "system = (\n",
-    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
-    ")\n",
-    "human = \"{text}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"Korean\",\n",
-    "        \"text\": \"I love Python\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19e53d75935143fd",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "ChatAnthropicMessages also requires the anthropic_api_key argument, or the ANTHROPIC_API_KEY environment variable must be set. \n",
-    "\n",
-    "ChatAnthropicMessages also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e20a139d30e3d333",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:26.012325Z",
-     "start_time": "2024-01-19T11:25:25.288358Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chain.ainvoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"Korean\",\n",
-    "        \"text\": \"I love Python\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "6f34f1073d7e7120",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T11:25:28.323455Z",
-     "start_time": "2024-01-19T11:25:26.012040Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are some of the most famous tourist attractions in Japan:\n",
-      "\n",
-      "- Tokyo Tower - A communication and observation tower in Tokyo modeled after the Eiffel Tower. It offers stunning views of the city.\n",
-      "\n",
-      "- Mount Fuji - Japan's highest and most famous mountain. It's a iconic symbol of Japan and a UNESCO World Heritage Site. \n",
-      "\n",
-      "- Itsukushima Shrine (Miyajima) - A shrine located on an island in Hiroshima prefecture, known for its \"floating\" torii gate that seems to float on water during high tide.\n",
-      "\n",
-      "- Himeji Castle - A UNESCO World Heritage Site famous for having withstood numerous battles without destruction to its intricate white walls and sloping, triangular roofs. \n",
-      "\n",
-      "- Kawaguchiko Station - Near Mount Fuji, this area is known for its scenic Fuji Five Lakes region. \n",
-      "\n",
-      "- Hiroshima Peace Memorial Park and Museum - Commemorates the world's first atomic bombing in Hiroshima on August 6, 1945. \n",
-      "\n",
-      "- Arashiyama Bamboo Grove - A renowned bamboo forest located in Kyoto that draws many visitors.\n",
-      "\n",
-      "- Kegon Falls - One of Japan's largest waterfalls"
-     ]
-    }
-   ],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"human\", \"Give me a list of famous tourist attractions in Japan\")]\n",
-    ")\n",
-    "chain = prompt | chat\n",
-    "for chunk in chain.stream({}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
+    "chat.invoke(messages)"
    ]
   }
  ],

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -15,7 +15,16 @@
    "execution_count": 1,
    "id": "378be79b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_experimental.llms.anthropic_functions import AnthropicFunctions"
    ]
@@ -32,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e1d535f6",
    "metadata": {},
    "outputs": [],
@@ -93,7 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = model.invoke(\n",
+    "response = model.predict_messages(\n",
     "    [HumanMessage(content=\"whats the weater in boston?\")], functions=functions\n",
     ")"
    ]
@@ -131,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "7af5c567",
    "metadata": {},
    "outputs": [],
@@ -153,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "bd01082a",
    "metadata": {},
    "outputs": [],
@@ -163,12 +172,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b5a23e9f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
+       " {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "chain.invoke(inp)"
+    "chain.run(inp)"
    ]
   },
   {
@@ -235,7 +256,7 @@
     }
    ],
    "source": [
-    "chain.invoke(\"this is really cool\")"
+    "chain.run(\"this is really cool\")"
    ]
   }
  ],
@@ -255,7 +276,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/azureml_chat_endpoint.ipynb

@@ -15,9 +15,9 @@
    "source": [
     "# AzureMLChatOnlineEndpoint\n",
     "\n",
-    ">[Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides foundational and general purpose models from different providers.\n",
+    ">[Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. `Azure Foundation Models` include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
     ">\n",
-    ">In general, you need to deploy models in order to consume its predictions (inference). In `Azure Machine Learning`, [Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints) are used to deploy these models with a real-time serving. They are based on the ideas of `Endpoints` and `Deployments` which allow you to decouple the interface of your production workload from the implementation that serves it.\n",
+    ">[Azure Machine Learning Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints). After you train machine learning models or pipelines, you need to deploy them to production so that others can use them for inference. Inference is the process of applying new input data to the machine learning model or pipeline to generate outputs. While these outputs are typically referred to as \"predictions,\" inferencing can be used to generate outputs for other machine learning tasks, such as classification and clustering. In `Azure Machine Learning`, you perform inferencing by using endpoints and deployments. `Endpoints` and `Deployments` allow you to decouple the interface of your production workload from the implementation that serves it.\n",
     "\n",
     "This notebook goes over how to use a chat model hosted on an `Azure Machine Learning Endpoint`."
    ]
@@ -37,11 +37,10 @@
    "source": [
     "## Set up\n",
     "\n",
-    "You must [deploy a model on Azure ML](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-foundation-models?view=azureml-api-2#deploying-foundation-models-to-endpoints-for-inferencing) or [to Azure AI studio](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-open) and obtain the following parameters:\n",
+    "To use the wrapper, you must [deploy a model on AzureML](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-foundation-models?view=azureml-api-2#deploying-foundation-models-to-endpoints-for-inferencing) and obtain the following parameters:\n",
     "\n",
-    "* `endpoint_url`: The REST endpoint url provided by the endpoint.\n",
-    "* `endpoint_api_type`: Use `endpoint_type='realtime'` when deploying models to **Realtime endpoints** (hosted managed infrastructure). Use `endpoint_type='serverless'` when deploying models using the **Pay-as-you-go** offering (model as a service).\n",
-    "* `endpoint_api_key`: The API key provided by the endpoint"
+    "* `endpoint_api_key`: The API key provided by the endpoint\n",
+    "* `endpoint_url`: The REST endpoint url provided by the endpoint"
    ]
   },
   {
@@ -52,40 +51,7 @@
     "\n",
     "The `content_formatter` parameter is a handler class for transforming the request and response of an AzureML endpoint to match with required schema. Since there are a wide range of models in the model catalog, each of which may process data differently from one another, a `ContentFormatterBase` class is provided to allow users to transform data to their liking. The following content formatters are provided:\n",
     "\n",
-    "* `LLamaChatContentFormatter`: Formats request and response data for LLaMa2-chat\n",
-    "\n",
-    "*Note: `langchain.chat_models.azureml_endpoint.LLamaContentFormatter` is being deprecated and replaced with `langchain.chat_models.azureml_endpoint.LLamaChatContentFormatter`.*\n",
-    "\n",
-    "You can implement custom content formatters specific for your model deriving from the class `langchain_community.llms.azureml_endpoint.ContentFormatterBase`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Examples\n",
-    "\n",
-    "The following section cotain examples about how to use this class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models.azureml_endpoint import (\n",
-    "    AzureMLEndpointApiType,\n",
-    "    LlamaChatContentFormatter,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example: Chat completions with real-time endpoints"
+    "* `LLamaContentFormatter`: Formats request and response data for LLaMa2-chat"
    ]
   },
   {
@@ -110,79 +76,11 @@
     "\n",
     "chat = AzureMLChatOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/score\",\n",
-    "    endpoint_api_type=AzureMLEndpointApiType.realtime,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=LlamaChatContentFormatter(),\n",
+    "    content_formatter=LlamaContentFormatter,\n",
     ")\n",
-    "response = chat.invoke(\n",
-    "    [HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")]\n",
-    ")\n",
-    "response"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example: Chat completions with pay-as-you-go deployments (model as a service)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = AzureMLChatOnlineEndpoint(\n",
-    "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions\",\n",
-    "    endpoint_api_type=AzureMLEndpointApiType.serverless,\n",
-    "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=LlamaChatContentFormatter,\n",
-    ")\n",
-    "response = chat.invoke(\n",
-    "    [HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")]\n",
-    ")\n",
-    "response"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you need to pass additional parameters to the model, use `model_kwards` argument:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = AzureMLChatOnlineEndpoint(\n",
-    "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions\",\n",
-    "    endpoint_api_type=AzureMLEndpointApiType.serverless,\n",
-    "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=LlamaChatContentFormatter,\n",
-    "    model_kwargs={\"temperature\": 0.8},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Parameters can also be passed during invocation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response = chat.invoke(\n",
-    "    [HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")],\n",
-    "    max_tokens=512,\n",
+    "response = chat(\n",
+    "    messages=[HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")]\n",
     ")\n",
     "response"
    ]

docs/docs/integrations/chat/baichuan.ipynb

@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chat with Baichuan-192K\n",
+    "# ChatBaichuan\n",
     "\n",
     "Baichuan chat models API by Baichuan Intelligent Technology. For more information, see [https://platform.baichuan-ai.com/docs/api](https://platform.baichuan-ai.com/docs/api)"
    ]
@@ -44,25 +44,20 @@
    },
    "outputs": [],
    "source": [
-    "chat = ChatBaichuan(baichuan_api_key=\"YOUR_API_KEY\")"
+    "chat = ChatBaichuan(\n",
+    "    baichuan_api_key=\"YOUR_API_KEY\", baichuan_secret_key=\"YOUR_SECRET_KEY\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, you can set your API key with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"BAICHUAN_API_KEY\"] = \"YOUR_API_KEY\""
+    "or you can set `api_key` and `secret_key` in your environment variables\n",
+    "```bash\n",
+    "export BAICHUAN_API_KEY=YOUR_API_KEY\n",
+    "export BAICHUAN_SECRET_KEY=YOUR_SECRET_KEY\n",
+    "```"
    ]
   },
   {
@@ -96,7 +91,7 @@
     "collapsed": false
    },
    "source": [
-    "## Chat with Baichuan-192K with Streaming"
+    "## For ChatBaichuan with Streaming"
    ]
   },
   {
@@ -113,6 +108,7 @@
    "source": [
     "chat = ChatBaichuan(\n",
     "    baichuan_api_key=\"YOUR_API_KEY\",\n",
+    "    baichuan_secret_key=\"YOUR_SECRET_KEY\",\n",
     "    streaming=True,\n",
     ")"
    ]

docs/docs/integrations/chat/deepinfra.ipynb

@@ -1,224 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# DeepInfra\n",
-    "\n",
-    "[DeepInfra](https://deepinfra.com/?utm_source=langchain) is a serverless inference as a service that provides access to a [variety of LLMs](https://deepinfra.com/models?utm_source=langchain) and [embeddings models](https://deepinfra.com/models?type=embeddings&utm_source=langchain). This notebook goes over how to use LangChain with DeepInfra for chat models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set the Environment API Key\n",
-    "Make sure to get your API key from DeepInfra. You have to [Login](https://deepinfra.com/login?from=%2Fdash) and get a new token.\n",
-    "\n",
-    "You are given a 1 hour free of serverless GPU compute to test different models. (see [here](https://github.com/deepinfra/deepctl#deepctl))\n",
-    "You can print your token with `deepctl auth token`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a new token: https://deepinfra.com/login?from=%2Fdash\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "DEEPINFRA_API_TOKEN = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# or pass deepinfra_api_token parameter to the ChatDeepInfra constructor\n",
-    "os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatDeepInfra\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatDeepInfra(model=\"meta-llama/Llama-2-7b-chat-hf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatDeepInfra` also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.agenerate([messages])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " J'aime la programmation."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatDeepInfra(\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/edenai.ipynb

@@ -1,272 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Eden AI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This example goes over how to use LangChain to interact with Eden AI models\n",
-    "\n",
-    "-----------------------------------------------------------------------------------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`EdenAI` goes beyond mere model invocation. It empowers you with advanced features, including:\n",
-    "\n",
-    "- **Multiple Providers**: Gain access to a diverse range of language models offered by various providers, giving you the freedom to choose the best-suited model for your use case.\n",
-    "\n",
-    "- **Fallback Mechanism**: Set a fallback mechanism to ensure seamless operations even if the primary provider is unavailable, you can easily switches to an alternative provider.\n",
-    "\n",
-    "- **Usage Tracking**: Track usage statistics on a per-project and per-API key basis. This feature allows you to monitor and manage resource consumption effectively.\n",
-    "\n",
-    "- **Monitoring and Observability**: `EdenAI` provides comprehensive monitoring and observability tools on the platform. Monitor the performance of your language models, analyze usage patterns, and gain valuable insights to optimize your applications.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Accessing the EDENAI's API requires an API key, \n",
-    "\n",
-    "which you can get by creating an account https://app.edenai.run/user/register  and heading here https://app.edenai.run/admin/iam/api-keys\n",
-    "\n",
-    "Once we have a key we'll want to set it as an environment variable by running:\n",
-    "\n",
-    "```bash\n",
-    "export EDENAI_API_KEY=\"...\"\n",
-    "```\n",
-    "\n",
-    "You can find more details on the API reference : https://docs.edenai.co/reference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you'd prefer not to set an environment variable you can pass the key in directly via the edenai_api_key named parameter\n",
-    "\n",
-    " when initiating the EdenAI Chat Model class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.edenai import ChatEdenAI\n",
-    "from langchain_core.messages import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatEdenAI(\n",
-    "    edenai_api_key=\"...\", provider=\"openai\", temperature=0.2, max_tokens=250\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [HumanMessage(content=\"Hello !\")]\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.ainvoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Streaming and Batching\n",
-    "\n",
-    "`ChatEdenAI` supports streaming and batching. Below is an example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello! How can I assist you today?"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in chat.stream(messages):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='Hello! How can I assist you today?')]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat.batch([messages])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fallback mecanism\n",
-    "\n",
-    "With Eden AI you can set a fallback mechanism to ensure seamless operations even if the primary provider is unavailable, you can easily switches to an alternative provider."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatEdenAI(\n",
-    "    edenai_api_key=\"...\",\n",
-    "    provider=\"openai\",\n",
-    "    temperature=0.2,\n",
-    "    max_tokens=250,\n",
-    "    fallback_providers=\"google\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, you can use Google as a backup provider if OpenAI encounters any issues.\n",
-    "\n",
-    "For more information and details about Eden AI, check out this link: : https://docs.edenai.co/docs/additional-parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chaining Calls\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\n",
-    "    \"What is a good name for a company that makes {product}?\"\n",
-    ")\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='VitalBites')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"product\": \"healthy snacks\"})"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain-pr",
-   "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -320,57 +320,11 @@
     "4. Message may be blocked if they violate the safety checks of the LLM. In this case, the model will return an empty response."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "54793b9e",
-   "metadata": {},
-   "source": [
-    "### Safety Settings\n",
-    "\n",
-    "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "75fdfad6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_genai import (\n",
-    "    ChatGoogleGenerativeAI,\n",
-    "    HarmBlockThreshold,\n",
-    "    HarmCategory,\n",
-    ")\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-pro\",\n",
-    "    safety_settings={\n",
-    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e68e203d",
-   "metadata": {},
-   "source": [
-    "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "92b5aca5",
    "metadata": {},
-   "source": [
-    "## Additional Configuration\n",
-    "\n",
-    "You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:\n",
-    "\n",
-    "- `client_options`: [Client Options](https://googleapis.dev/python/google-api-core/latest/client_options.html#module-google.api_core.client_options) to pass to the Google API Client, such as a custom `client_options[\"api_endpoint\"]`\n",
-    "- `transport`: The transport method to use, such as `rest`, `grpc`, or `grpc_asyncio`."
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -11,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -18,14 +19,6 @@
     "\n",
     "Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
     "\n",
-    "ChatVertexAI exposes all foundational models available in Google Cloud:\n",
-    "\n",
-    "- Gemini (`gemini-pro` and `gemini-pro-vision`)\n",
-    "- PaLM 2 for Text (`text-bison`)\n",
-    "- Codey for Code Generation (`codechat-bison`)\n",
-    "\n",
-    "For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
-    "\n",
     "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) customer data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
     "\n",
     "To use `Google Cloud Vertex AI` PaLM you must have the `langchain-google-vertexai` Python package installed and either:\n",
@@ -42,16 +35,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install --upgrade --quiet  langchain-google-vertexai"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -70,7 +76,7 @@
        "AIMessage(content=\" J'aime la programmation.\")"
       ]
      },
-     "execution_count": null,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,40 +92,6 @@
     "chain.invoke({})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Gemini doesn't support SystemMessage at the moment, but it can be added to the first human message in the row. If you want such behavior, just set the `convert_system_message_to_human` to `True`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'aime la programmation.\")"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = \"You are a helpful assistant who translate English to French\"\n",
-    "human = \"Translate this sentence from English to French. I love programming.\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chat = ChatVertexAI(model_name=\"gemini-pro\", convert_system_message_to_human=True)\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke({})"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -129,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +110,7 @@
        "AIMessage(content=' プログラミングが大好きです')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,8 +122,6 @@
     "human = \"{text}\"\n",
     "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
     "\n",
-    "chat = ChatVertexAI()\n",
-    "\n",
     "chain = prompt | chat\n",
     "\n",
     "chain.invoke(\n",
@@ -164,18 +134,30 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-17T21:09:25.423568Z",
+     "iopub.status.busy": "2023-06-17T21:09:25.423213Z",
+     "iopub.status.idle": "2023-06-17T21:09:25.429641Z",
+     "shell.execute_reply": "2023-06-17T21:09:25.429060Z",
+     "shell.execute_reply.started": "2023-06-17T21:09:25.423546Z"
+    },
+    "tags": []
+   },
    "source": [
     "## Code generation chat models\n",
-    "You can now leverage the Codey API for code chat within Vertex AI. The model available is:\n",
-    "- `codechat-bison`: for code assistance"
+    "You can now leverage the Codey API for code chat within Vertex AI. The model name is:\n",
+    "- codechat-bison: for code assistance"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -183,51 +165,27 @@
      "text": [
       " ```python\n",
       "def is_prime(n):\n",
-      "  \"\"\"\n",
-      "  Check if a number is prime.\n",
-      "\n",
-      "  Args:\n",
-      "    n: The number to check.\n",
-      "\n",
-      "  Returns:\n",
-      "    True if n is prime, False otherwise.\n",
-      "  \"\"\"\n",
-      "\n",
-      "  # If n is 1, it is not prime.\n",
-      "  if n == 1:\n",
-      "    return False\n",
-      "\n",
-      "  # Iterate over all numbers from 2 to the square root of n.\n",
-      "  for i in range(2, int(n ** 0.5) + 1):\n",
-      "    # If n is divisible by any number from 2 to its square root, it is not prime.\n",
-      "    if n % i == 0:\n",
-      "      return False\n",
-      "\n",
-      "  # If n is divisible by no number from 2 to its square root, it is prime.\n",
-      "  return True\n",
-      "\n",
+      "    if n <= 1:\n",
+      "        return False\n",
+      "    for i in range(2, n):\n",
+      "        if n % i == 0:\n",
+      "            return False\n",
+      "    return True\n",
       "\n",
       "def find_prime_numbers(n):\n",
-      "  \"\"\"\n",
-      "  Find all prime numbers up to a given number.\n",
+      "    prime_numbers = []\n",
+      "    for i in range(2, n + 1):\n",
+      "        if is_prime(i):\n",
+      "            prime_numbers.append(i)\n",
+      "    return prime_numbers\n",
       "\n",
-      "  Args:\n",
-      "    n: The upper bound for the prime numbers to find.\n",
+      "print(find_prime_numbers(100))\n",
+      "```\n",
       "\n",
-      "  Returns:\n",
-      "    A list of all prime numbers up to n.\n",
-      "  \"\"\"\n",
+      "Output:\n",
       "\n",
-      "  # Create a list of all numbers from 2 to n.\n",
-      "  numbers = list(range(2, n + 1))\n",
-      "\n",
-      "  # Iterate over the list of numbers and remove any that are not prime.\n",
-      "  for number in numbers:\n",
-      "    if not is_prime(number):\n",
-      "      numbers.remove(number)\n",
-      "\n",
-      "  # Return the list of prime numbers.\n",
-      "  return numbers\n",
+      "```\n",
+      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
       "```\n"
      ]
     }
@@ -237,159 +195,22 @@
     "    model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
     ")\n",
     "\n",
-    "message = chat.invoke(\"Write a Python function generating all prime numbers\")\n",
+    "message = chat.invoke(\"Write a Python function to identify all prime numbers\")\n",
     "print(message.content)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Full generation info\n",
-    "\n",
-    "We can use the `generate` method to get back extra metadata like [safety attributes](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_confidence_scoring) and not just chat completions\n",
-    "\n",
-    "Note that the `generation_info` will be different depending if you're using a gemini model or not.\n",
-    "\n",
-    "### Gemini model\n",
-    "\n",
-    "`generation_info` will include:\n",
-    "\n",
-    "- `is_blocked`: whether generation was blocked or not\n",
-    "- `safety_ratings`: safety ratings' categories and probability labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'is_blocked': False,\n",
-      " 'safety_ratings': [{'category': 'HARM_CATEGORY_HARASSMENT',\n",
-      "                     'probability_label': 'NEGLIGIBLE'},\n",
-      "                    {'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
-      "                     'probability_label': 'NEGLIGIBLE'},\n",
-      "                    {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
-      "                     'probability_label': 'NEGLIGIBLE'},\n",
-      "                    {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
-      "                     'probability_label': 'NEGLIGIBLE'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pprint import pprint\n",
-    "\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_google_vertexai import ChatVertexAI, HarmBlockThreshold, HarmCategory\n",
-    "\n",
-    "human = \"Translate this sentence from English to French. I love programming.\"\n",
-    "messages = [HumanMessage(content=human)]\n",
-    "\n",
-    "\n",
-    "chat = ChatVertexAI(\n",
-    "    model_name=\"gemini-pro\",\n",
-    "    safety_settings={\n",
-    "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "result = chat.generate([messages])\n",
-    "pprint(result.generations[0][0].generation_info)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Non-gemini model\n",
-    "\n",
-    "`generation_info` will include:\n",
-    "\n",
-    "- `is_blocked`: whether generation was blocked or not\n",
-    "- `safety_attributes`: a dictionary mapping safety attributes to their scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'is_blocked': False,\n",
-      " 'safety_attributes': {'Derogatory': 0.1,\n",
-      "                       'Finance': 0.3,\n",
-      "                       'Insult': 0.1,\n",
-      "                       'Sexual': 0.1}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "chat = ChatVertexAI()  # default is `chat-bison`\n",
-    "\n",
-    "result = chat.generate([messages])\n",
-    "pprint(result.generations[0][0].generation_info)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Function Calling with Gemini\n",
-    "\n",
-    "We can call Gemini models with tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MyModel(name='Erick', age=27)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.pydantic_v1 import BaseModel\n",
-    "from langchain_google_vertexai import create_structured_runnable\n",
-    "\n",
-    "llm = ChatVertexAI(model_name=\"gemini-pro\")\n",
-    "\n",
-    "\n",
-    "class MyModel(BaseModel):\n",
-    "    name: str\n",
-    "    age: int\n",
-    "\n",
-    "\n",
-    "chain = create_structured_runnable(MyModel, llm)\n",
-    "chain.invoke(\"My name is Erick and I'm 27 years old\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Asynchronous calls\n",
     "\n",
-    "We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)."
+    "We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,16 +224,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' अहं प्रोग्रामनं प्रेमामि')"
+       "AIMessage(content=' Why do you love programming?')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -423,8 +244,6 @@
     ")\n",
     "human = \"{text}\"\n",
     "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chat = ChatVertexAI(model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5)\n",
     "chain = prompt | chat\n",
     "\n",
     "asyncio.run(\n",
@@ -449,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -480,15 +299,37 @@
     "    sys.stdout.write(chunk.content)\n",
     "    sys.stdout.flush()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "",
-   "name": ""
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
+   }
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/huggingface.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hugging Face\n",
+    "# Hugging Face Chat Wrapper\n",
     "\n",
-    "This notebook shows how to get started using `Hugging Face` LLM's as chat models.\n",
+    "This notebook shows how to get started using Hugging Face LLM's as chat models.\n",
     "\n",
     "In particular, we will:\n",
     "1. Utilize the [HuggingFaceTextGenInference](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_text_gen_inference.py), [HuggingFaceEndpoint](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_endpoint.py), or [HuggingFaceHub](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_hub.py) integrations to instantiate an `LLM`.\n",
@@ -26,6 +26,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.3.1 is available.\n",
+      "You should consider upgrading via the '/Users/jacoblee/langchain/langchain/libs/langchain/.venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
       "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -47,14 +49,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `HuggingFaceTextGenInference`"
+    "#### `HuggingFaceTextGenInference`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jacoblee/langchain/langchain/libs/langchain/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -82,7 +93,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `HuggingFaceEndpoint`"
+    "#### `HuggingFaceEndpoint`"
    ]
   },
   {
@@ -110,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `HuggingFaceHub`"
+    "#### `HuggingFaceHub`"
    ]
   },
   {
@@ -280,7 +291,7 @@
    "source": [
     "## 3. Take it for a spin as an agent!\n",
     "\n",
-    "Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
+    "Here we'll test out `Zephyr-7B-beta` as a zero-shot ReAct Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
     "\n",
     "> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
    ]
@@ -437,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/konko.ipynb

@@ -15,21 +15,23 @@
    "source": [
     "# ChatKonko\n",
     "\n",
-    "# Konko\n",
-    "\n",
     ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
     "\n",
+    "Konko API is a fully managed API designed to help application developers:\n",
     "\n",
-    "1. **Select** the right open source or proprietary LLMs for their application\n",
-    "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n",
-    "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n",
-    "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n",
+    "1. Select the right LLM(s) for their application\n",
+    "2. Prototype with various open-source and proprietary LLMs\n",
+    "3. Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure\n",
     "\n",
     "\n",
-    "This example goes over how to use LangChain to interact with `Konko` ChatCompletion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-chatcompletion)\n",
-    "\n",
-    "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models\n",
-    "\n"
+    "This example goes over how to use LangChain to interact with `Konko` [models](https://docs.konko.ai/docs/overview)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this notebook, you'll need Konko API key. You can request it by messaging support@konko.ai."
    ]
   },
   {
@@ -48,7 +50,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Set Environment Variables\n",
+    "## 2. Set API Keys\n",
+    "\n",
+    "<br />\n",
+    "\n",
+    "### Option 1: Set Environment Variables\n",
     "\n",
     "1. You can set environment variables for \n",
     "   1. KONKO_API_KEY (Required)\n",
@@ -58,7 +64,18 @@
     "```shell\n",
     "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n",
     "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n",
-    "```"
+    "```\n",
+    "\n",
+    "Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.\n",
+    "\n",
+    "### Option 2: Set API Keys Programmatically\n",
+    "\n",
+    "If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:\n",
+    "\n",
+    "```python\n",
+    "konko.set_api_key('your_KONKO_API_KEY_here')  \n",
+    "konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional\n",
+    "```\n"
    ]
   },
   {
@@ -67,34 +84,36 @@
    "source": [
     "## Calling a model\n",
     "\n",
-    "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n",
+    "Find a model on the [Konko overview page](https://docs.konko.ai/docs/overview)\n",
     "\n",
-    "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n",
+    "For example, for this [LLama 2 model](https://docs.konko.ai/docs/meta-llama-2-13b-chat). The model id would be: `\"meta-llama/Llama-2-13b-chat-hf\"`\n",
+    "\n",
+    "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/listmodels).\n",
     "\n",
     "From here, we can initialize our model:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "chat = ChatKonko(max_tokens=400, model=\"meta-llama/llama-2-13b-chat\")"
+    "chat = ChatKonko(max_tokens=400, model=\"meta-llama/Llama-2-13b-chat-hf\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"  Sure thing! The Big Bang Theory is a scientific theory that explains the origins of the universe. In short, it suggests that the universe began as an infinitely hot and dense point around 13.8 billion years ago and expanded rapidly. This expansion continues to this day, and it's what makes the universe look the way it does.\\n\\nHere's a brief overview of the key points:\\n\\n1. The universe started as a singularity, a point of infinite density and temperature.\\n2. The singularity expanded rapidly, causing the universe to cool and expand.\\n3. As the universe expanded, particles began to form, including protons, neutrons, and electrons.\\n4. These particles eventually came together to form atoms, and later, stars and galaxies.\\n5. The universe is still expanding today, and the rate of this expansion is accelerating.\\n\\nThat's the Big Bang Theory in a nutshell! It's a pretty mind-blowing idea when you think about it, and it's supported by a lot of scientific evidence. Do you have any other questions about it?\")"
+       "AIMessage(content=\" Sure, I'd be happy to explain the Big Bang Theory briefly!\\n\\nThe Big Bang Theory is the leading explanation for the origin and evolution of the universe, based on a vast amount of observational evidence from many fields of science. In essence, the theory posits that the universe began as an infinitely hot and dense point, known as a singularity, around 13.8 billion years ago. This singularity expanded rapidly, and as it did, it cooled and formed subatomic particles, which eventually coalesced into the first atoms, and later into the stars and galaxies we see today.\\n\\nThe theory gets its name from the idea that the universe began in a state of incredibly high energy and temperature, and has been expanding and cooling ever since. This expansion is thought to have been driven by a mysterious force known as dark energy, which is thought to be responsible for the accelerating expansion of the universe.\\n\\nOne of the key predictions of the Big Bang Theory is that the universe should be homogeneous and isotropic on large scales, meaning that it should look the same in all directions and have the same properties everywhere. This prediction has been confirmed by a wealth of observational evidence, including the cosmic microwave background radiation, which is thought to be a remnant of the early universe.\\n\\nOverall, the Big Bang Theory is a well-established and widely accepted explanation for the origins of the universe, and it has been supported by a vast amount of observational evidence from many fields of science.\", additional_kwargs={}, example=False)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,6 +125,13 @@
     "]\n",
     "chat(messages)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/chat/litellm_router.ipynb

@@ -1,218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "59148044",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: LiteLLM Router\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "247da7a6",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# ChatLiteLLMRouter\n",
-    "\n",
-    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
-    "\n",
-    "This notebook covers how to get started with using Langchain + the LiteLLM Router I/O library. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ChatLiteLLMRouter\n",
-    "from litellm import Router"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_list = [\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-4\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-4-1106-preview\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-4\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-4-1106-preview\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "litellm_router = Router(model_list=model_list)\n",
-    "chat = ChatLiteLLMRouter(router=litellm_router)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'aime programmer.\")"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatLiteLLMRouter` also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\"J'adore programmer.\", generation_info={'finish_reason': 'stop'}, message=AIMessage(content=\"J'adore programmer.\"))]], llm_output={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 19, 'total_tokens': 25}, 'model_name': None}, run=[RunInfo(run_id=UUID('75003ec9-1e2b-43b7-a216-10dcc0f75e00'))])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.agenerate([messages])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\")"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatLiteLLMRouter(\n",
-    "    router=litellm_router,\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/mistralai.ipynb

@@ -15,53 +15,16 @@
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# MistralAI\n",
+    "# ChatMistralAI\n",
     "\n",
     "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n",
     "\n",
-    "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n",
-    "\n",
-    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc686b8f",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "You will need the `langchain-core` and `langchain-mistralai` package to use the API. You can install these with:\n",
-    "\n",
-    "```bash\n",
-    "pip install -U langchain-core langchain-mistralai\n",
-    "\n",
-    "We'll also need to get a [Mistral API key](https://console.mistral.ai/users/api-keys/)"
+    "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "c3fd4184",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "\n",
-    "mistral_api_key = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "502127fd",
-   "metadata": {},
-   "source": [
-    "## Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "tags": []
@@ -74,20 +37,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "import os\n",
+    "\n",
+    "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n",
     "# If mistral_api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n",
     "chat = ChatMistralAI(mistral_api_key=mistral_api_key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
     "tags": []
@@ -96,16 +62,16 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Who's there? I was just about to ask the same thing! How can I assist you today?\")"
+       "AIMessage(content=\"Hello! I'm here to assist you. How can I help you today? If you have any questions or need information on a particular topic, feel free to ask. I'm ready to provide accurate and helpful answers to the best of my ability.\")"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "messages = [HumanMessage(content=\"knock knock\")]\n",
+    "messages = [HumanMessage(content=\"say a brief hello\")]\n",
     "chat.invoke(messages)"
    ]
   },
@@ -114,12 +80,12 @@
    "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
    "metadata": {},
    "source": [
-    "### Async"
+    "## `ChatMistralAI` also supports async and streaming functionality:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
     "tags": []
@@ -128,10 +94,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Who\\'s there?\\n\\n(You can then continue the \"knock knock\" joke by saying the name of the person or character who should be responding. For example, if I say \"Banana,\" you could respond with \"Banana who?\" and I would say \"Banana bunch! Get it? Because a group of bananas is called a \\'bunch\\'!\" and then we would both laugh and have a great time. But really, you can put anything you want in the spot where I put \"Banana\" and it will still technically be a \"knock knock\" joke. The possibilities are endless!)')"
+       "AIMessage(content=\"Hello! I'm glad you're here. If you have any questions or need assistance with something related to programming or software development, feel free to ask. I'll do my best to help you out. Have a great day!\")"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,17 +106,9 @@
     "await chat.ainvoke(messages)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "86ccef97",
-   "metadata": {},
-   "source": [
-    "### Streaming\n"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
    "metadata": {
     "tags": []
@@ -160,27 +118,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Who's there?\n",
-      "\n",
-      "(After this, the conversation can continue as a call and response \"who's there\" joke. Here is an example of how it could go:\n",
-      "\n",
-      "You say: Orange.\n",
-      "I say: Orange who?\n",
-      "You say: Orange you glad I didn't say banana!?)\n",
-      "\n",
-      "But since you asked for a knock knock joke specifically, here's one for you:\n",
-      "\n",
-      "Knock knock.\n",
-      "\n",
-      "Me: Who's there?\n",
-      "\n",
-      "You: Lettuce.\n",
-      "\n",
-      "Me: Lettuce who?\n",
-      "\n",
-      "You: Lettuce in, it's too cold out here!\n",
-      "\n",
-      "I hope this brings a smile to your face! Do you have a favorite knock knock joke you'd like to share? I'd love to hear it."
+      "Hello! I'm happy to assist you. Is there a specific question or topic you would like to discuss? I can provide information and answer questions on a wide variety of subjects."
      ]
     }
    ],
@@ -188,79 +126,6 @@
     "for chunk in chat.stream(messages):\n",
     "    print(chunk.content, end=\"\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6189577",
-   "metadata": {},
-   "source": [
-    "### Batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e63aebcb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content=\"Who's there? I was just about to ask the same thing! Go ahead and tell me who's there. I love a good knock-knock joke.\")]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat.batch([messages])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38e39e71",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ee43a1ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0dc49212",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Why do bears hate shoes so much? They like to run around in their bear feet.')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"topic\": \"bears\"})"
-   ]
   }
  ],
  "metadata": {
@@ -279,7 +144,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/sparkllm.ipynb

@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3ddface67cd10a87",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# SparkLLM Chat\n",
-    "\n",
-    "SparkLLM chat models API by iFlyTek. For more information, see [iFlyTek Open Platform](https://www.xfyun.cn/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Basic use"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43daa39972d4c533",
-   "metadata": {
-    "collapsed": false,
-    "is_executing": true
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"For basic init and call\"\"\"\n",
-    "from langchain.chat_models import ChatSparkLLM\n",
-    "from langchain.schema import HumanMessage\n",
-    "\n",
-    "chat = ChatSparkLLM(\n",
-    "    spark_app_id=\"<app_id>\", spark_api_key=\"<api_key>\", spark_api_secret=\"<api_secret>\"\n",
-    ")\n",
-    "message = HumanMessage(content=\"Hello\")\n",
-    "chat([message])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df755f4c5689510",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "- Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY` and `IFLYTEK_SPARK_API_SECRET` or pass parameters when creating `ChatSparkLLM` as the demo above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "984e32ee47bc6772",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## For ChatSparkLLM with Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7dc162bd65fec08f",
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatSparkLLM(streaming=True)\n",
-    "for chunk in chat.stream(\"Hello!\"):\n",
-    "    print(chunk.content, end=\"\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/yuan2.ipynb

@@ -1,463 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "source": [
-    "---\n",
-    "sidebar_label: YUAN2\n",
-    "---"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% raw\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# YUAN2.0\n",
-    "\n",
-    "This notebook shows how to use [YUAN2 API](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/docs/inference_server.md) in LangChain with the langchain.chat_models.ChatYuan2.\n",
-    "\n",
-    "[*Yuan2.0*](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md) is a new generation Fundamental Large Language Model developed by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan 2.0-51B, and Yuan 2.0-2B. And we provide relevant scripts for pretraining, fine-tuning, and inference services for other developers. Yuan2.0 is based on Yuan1.0, utilizing a wider range of high-quality pre training data and instruction fine-tuning datasets to enhance the model's understanding of semantics, mathematics, reasoning, code, knowledge, and other aspects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Getting started\n",
-    "### Installation\n",
-    "First, Yuan2.0 provided an OpenAI compatible API, and we integrate ChatYuan2 into langchain chat model by using OpenAI client.\n",
-    "Therefore, ensure the openai package is installed in your Python environment. Run the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Importing the Required Modules\n",
-    "After installation, import the necessary modules to your Python script:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models import ChatYuan2\n",
-    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Setting Up Your API server\n",
-    "Setting up your OpenAI compatible API server following [yuan2 openai api server](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md).\n",
-    "If you deployed api server locally, you can simply set `api_key=\"EMPTY\"` or anything you want.\n",
-    "Just make sure, the `api_base` is set correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "yuan2_api_key = \"your_api_key\"\n",
-    "yuan2_api_base = \"http://127.0.0.1:8001/v1\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Initialize the ChatYuan2 Model\n",
-    "Here's how to initialize the chat model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatYuan2(\n",
-    "    yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
-    "    temperature=1.0,\n",
-    "    model_name=\"yuan2\",\n",
-    "    max_retries=3,\n",
-    "    streaming=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Basic Usage\n",
-    "Invoke the model with system and human messages like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    SystemMessage(content=\"你是一个人工智能助手。\"),\n",
-    "    HumanMessage(content=\"你好，你是谁？\"),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "print(chat(messages))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Basic Usage with streaming\n",
-    "For continuous interaction, use the streaming feature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "chat = ChatYuan2(\n",
-    "    yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
-    "    temperature=1.0,\n",
-    "    model_name=\"yuan2\",\n",
-    "    max_retries=3,\n",
-    "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    ")\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"你是个旅游小助手。\"),\n",
-    "    HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Advanced Features\n",
-    "### Usage with async calls\n",
-    "\n",
-    "Invoke the model with non-blocking calls, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "async def basic_agenerate():\n",
-    "    chat = ChatYuan2(\n",
-    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
-    "        temperature=1.0,\n",
-    "        model_name=\"yuan2\",\n",
-    "        max_retries=3,\n",
-    "    )\n",
-    "    messages = [\n",
-    "        [\n",
-    "            SystemMessage(content=\"你是个旅游小助手。\"),\n",
-    "            HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
-    "        ]\n",
-    "    ]\n",
-    "\n",
-    "    result = await chat.agenerate(messages)\n",
-    "    print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "asyncio.run(basic_agenerate())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Usage with prompt template\n",
-    "\n",
-    "Invoke the model with non-blocking calls and used chat template like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "async def ainvoke_with_prompt_template():\n",
-    "    from langchain.prompts.chat import (\n",
-    "        ChatPromptTemplate,\n",
-    "    )\n",
-    "\n",
-    "    chat = ChatYuan2(\n",
-    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
-    "        temperature=1.0,\n",
-    "        model_name=\"yuan2\",\n",
-    "        max_retries=3,\n",
-    "    )\n",
-    "    prompt = ChatPromptTemplate.from_messages(\n",
-    "        [\n",
-    "            (\"system\", \"你是一个诗人，擅长写诗。\"),\n",
-    "            (\"human\", \"给我写首诗，主题是{theme}。\"),\n",
-    "        ]\n",
-    "    )\n",
-    "    chain = prompt | chat\n",
-    "    result = await chain.ainvoke({\"theme\": \"明月\"})\n",
-    "    print(f\"type(result): {type(result)}; {result}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "asyncio.run(ainvoke_with_prompt_template())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Usage with async calls in streaming\n",
-    "For non-blocking calls with streaming output, use the astream method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "async def basic_astream():\n",
-    "    chat = ChatYuan2(\n",
-    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
-    "        temperature=1.0,\n",
-    "        model_name=\"yuan2\",\n",
-    "        max_retries=3,\n",
-    "    )\n",
-    "    messages = [\n",
-    "        SystemMessage(content=\"你是个旅游小助手。\"),\n",
-    "        HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
-    "    ]\n",
-    "    result = chat.astream(messages)\n",
-    "    async for chunk in result:\n",
-    "        print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "asyncio.run(basic_astream())"
-   ]
-  }
- ],
- "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": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "## 1. Select a dataset\n",
     "\n",
-    "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/concepts#datasets).\n",
+    "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/datasets).\n",
     "\n",
     "For the sake of this tutorial, we will upload an existing dataset here that you can use."
    ]

docs/docs/integrations/document_loaders/athena.ipynb

@@ -1,110 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MwTWzDxYgbrR"
-   },
-   "source": [
-    "# Athena\n",
-    "\n",
-    "This notebooks goes over how to load documents from AWS Athena"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "F0zaLR3xgWmO"
-   },
-   "outputs": [],
-   "source": [
-    "! pip install boto3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "076NLjfngoWJ"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.athena import AthenaLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "XpMRQwU9gu44"
-   },
-   "outputs": [],
-   "source": [
-    "database_name = \"my_database\"\n",
-    "s3_output_path = \"s3://my_bucket/query_results/\"\n",
-    "query = \"SELECT * FROM my_table\"\n",
-    "profile_name = \"my_profile\"\n",
-    "\n",
-    "loader = AthenaLoader(\n",
-    "    query=query,\n",
-    "    database=database_name,\n",
-    "    s3_output_uri=s3_output_path,\n",
-    "    profile_name=profile_name,\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()\n",
-    "print(documents)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5IBapL3ejoEt"
-   },
-   "source": [
-    "Example with metadata columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wMx6nI1qjryD"
-   },
-   "outputs": [],
-   "source": [
-    "database_name = \"my_database\"\n",
-    "s3_output_path = \"s3://my_bucket/query_results/\"\n",
-    "query = \"SELECT * FROM my_table\"\n",
-    "profile_name = \"my_profile\"\n",
-    "metadata_columns = [\"_row\", \"_created_at\"]\n",
-    "\n",
-    "loader = AthenaLoader(\n",
-    "    query=query,\n",
-    "    database=database_name,\n",
-    "    s3_output_uri=s3_output_path,\n",
-    "    profile_name=profile_name,\n",
-    "    metadata_columns=metadata_columns,\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()\n",
-    "print(documents)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/document_loaders/cassandra.ipynb

@@ -1,276 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vm8vn9t8DvC_"
-   },
-   "source": [
-    "# Cassandra"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database.Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html)."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5WjXERXzFEhg"
-   },
-   "source": [
-    "## Overview"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "juAmbgoWD17u"
-   },
-   "source": [
-    "The Cassandra Document Loader returns a list of Langchain Documents from a Cassandra database.\n",
-    "\n",
-    "You must either provide a CQL query or a table name to retrieve the documents.\n",
-    "The Loader takes the following parameters:\n",
-    "\n",
-    "* table: (Optional) The table to load the data from.\n",
-    "* session: (Optional) The cassandra driver session. If not provided, the cassio resolved session will be used.\n",
-    "* keyspace: (Optional) The keyspace of the table. If not provided, the cassio resolved keyspace will be used.\n",
-    "* query: (Optional) The query used to load the data.\n",
-    "* page_content_mapper: (Optional) a function to convert a row to string page content. The default converts the row to JSON.\n",
-    "* metadata_mapper: (Optional) a function to convert a row to metadata dict.\n",
-    "* query_parameters: (Optional) The query parameters used when calling session.execute .\n",
-    "* query_timeout: (Optional) The query timeout used when calling session.execute .\n",
-    "* query_custom_payload: (Optional) The query custom_payload used when calling `session.execute`.\n",
-    "* query_execution_profile: (Optional) The query execution_profile used when calling `session.execute`.\n",
-    "* query_host: (Optional) The query host used when calling `session.execute`.\n",
-    "* query_execute_as: (Optional) The query execute_as used when calling `session.execute`."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load documents with the Document Loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import CassandraLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### Init from a cassandra driver Session\n",
-    "\n",
-    "You need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from cassandra.cluster import Cluster\n",
-    "\n",
-    "cluster = Cluster()\n",
-    "session = cluster.connect()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "You need to provide the name of an existing keyspace of the Cassandra instance:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "Creating the document loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T15:47:25.893037Z",
-     "start_time": "2024-01-19T15:47:25.889398Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "loader = CassandraLoader(\n",
-    "    table=\"movie_reviews\",\n",
-    "    session=session,\n",
-    "    keyspace=CASSANDRA_KEYSPACE,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T15:47:26.399472Z",
-     "start_time": "2024-01-19T15:47:26.389145Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-19T15:47:33.287783Z",
-     "start_time": "2024-01-19T15:47:33.277862Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\"  the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### Init from cassio\n",
-    "\n",
-    "It's also possible to use cassio to configure the session and keyspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import cassio\n",
-    "\n",
-    "cassio.init(contact_points=\"127.0.0.1\", keyspace=CASSANDRA_KEYSPACE)\n",
-    "\n",
-    "loader = CassandraLoader(\n",
-    "    table=\"movie_reviews\",\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Attribution statement\n",
-    "\n",
-    "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "5WjXERXzFEhg"
-   ],
-   "provenance": []
-  },
-  "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": "3.9.17"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/document_loaders/example_data/source_code/example.py

@@ -3,7 +3,7 @@ class MyClass:
         self.name = name
 
     def greet(self):
-        print(f"Hello, {self.name}!")  # noqa: T201
+        print(f"Hello, {self.name}!")
 
 
 def main():

docs/docs/integrations/document_loaders/github.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# GitHub\n",
     "\n",
-    "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). Also shows how you can load github files for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example."
+    "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example."
    ]
   },
   {
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,9 +100,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Creates GitHubLoader (#5257)\r\n",
+      "\r\n",
+      "GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.\r\n",
+      "\r\n",
+      "Fixes #5257\r\n",
+      "\r\n",
+      "Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:\r\n",
+      "DataLoaders\r\n",
+      "- @eyurtsev\r\n",
+      "\n",
+      "{'url': 'https://github.com/langchain-ai/langchain/pull/5408', 'title': 'DocumentLoader for GitHub', 'creator': 'UmerHA', 'created_at': '2023-05-29T14:50:53Z', 'comments': 0, 'state': 'open', 'labels': ['enhancement', 'lgtm', 'doc loader'], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5408, 'is_pull_request': True}\n"
+     ]
+    }
+   ],
    "source": [
     "print(docs[0].page_content)\n",
     "print(docs[0].metadata)"
@@ -124,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,68 +157,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "### System Info\n",
+      "\n",
+      "LangChain version = 0.0.167\r\n",
+      "Python version = 3.11.0\r\n",
+      "System = Windows 11 (using Jupyter)\n",
+      "\n",
+      "### Who can help?\n",
+      "\n",
+      "- @hwchase17\r\n",
+      "- @agola11\r\n",
+      "- @UmerHA (I have a fix ready, will submit a PR)\n",
+      "\n",
+      "### Information\n",
+      "\n",
+      "- [ ] The official example notebooks/scripts\n",
+      "- [X] My own modified scripts\n",
+      "\n",
+      "### Related Components\n",
+      "\n",
+      "- [X] LLMs/Chat Models\n",
+      "- [ ] Embedding Models\n",
+      "- [X] Prompts / Prompt Templates / Prompt Selectors\n",
+      "- [ ] Output Parsers\n",
+      "- [ ] Document Loaders\n",
+      "- [ ] Vector Stores / Retrievers\n",
+      "- [ ] Memory\n",
+      "- [ ] Agents / Agent Executors\n",
+      "- [ ] Tools / Toolkits\n",
+      "- [ ] Chains\n",
+      "- [ ] Callbacks/Tracing\n",
+      "- [ ] Async\n",
+      "\n",
+      "### Reproduction\n",
+      "\n",
+      "```\r\n",
+      "import os\r\n",
+      "os.environ[\"OPENAI_API_KEY\"] = \"...\"\r\n",
+      "\r\n",
+      "from langchain.chains import LLMChain\r\n",
+      "from langchain_openai import ChatOpenAI\r\n",
+      "from langchain.prompts import PromptTemplate\r\n",
+      "from langchain.prompts.chat import ChatPromptTemplate\r\n",
+      "from langchain.schema import messages_from_dict\r\n",
+      "\r\n",
+      "role_strings = [\r\n",
+      "    (\"system\", \"you are a bird expert\"), \r\n",
+      "    (\"human\", \"which bird has a point beak?\")\r\n",
+      "]\r\n",
+      "prompt = ChatPromptTemplate.from_role_strings(role_strings)\r\n",
+      "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\r\n",
+      "chain.run({})\r\n",
+      "```\n",
+      "\n",
+      "### Expected behavior\n",
+      "\n",
+      "Chain should run\n",
+      "{'url': 'https://github.com/langchain-ai/langchain/issues/5027', 'title': \"ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings\", 'creator': 'UmerHA', 'created_at': '2023-05-20T10:39:18Z', 'comments': 1, 'state': 'open', 'labels': [], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5027, 'is_pull_request': False}\n"
+     ]
+    }
+   ],
    "source": [
     "print(docs[0].page_content)\n",
     "print(docs[0].metadata)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Github File Content\n",
-    "\n",
-    "For below code, loads all markdown file in rpeo `langchain-ai/langchain`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import GithubFileLoader"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "loader = GithubFileLoader(\n",
-    "    repo=\"langchain-ai/langchain\",  # the repo name\n",
-    "    access_token=ACCESS_TOKEN,\n",
-    "    github_api_url=\"https://api.github.com\",\n",
-    "    file_filter=lambda file_path: file_path.endswith(\n",
-    "        \".md\"\n",
-    "    ),  # load all markdowns files.\n",
-    ")\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "example output of one of document: \n",
-    "\n",
-    "```json\n",
-    "documents.metadata: \n",
-    "    {\n",
-    "      \"path\": \"README.md\",\n",
-    "      \"sha\": \"82f1c4ea88ecf8d2dfsfx06a700e84be4\",\n",
-    "      \"source\": \"https://github.com/langchain-ai/langchain/blob/master/README.md\"\n",
-    "    }\n",
-    "documents.content:\n",
-    "    mock content\n",
-    "```"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -219,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/pebblo.ipynb

@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pebblo Safe DocumentLoader\n",
-    "\n",
-    "> [Pebblo](https://github.com/daxa-ai/pebblo) enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.\n",
-    "\n",
-    "Pebblo has two components.\n",
-    "\n",
-    "1. Pebblo Safe DocumentLoader for Langchain\n",
-    "1. Pebblo Daemon\n",
-    "\n",
-    "This document describes how to augment your existing Langchain DocumentLoader with Pebblo Safe DocumentLoader to get deep data visibility on the types of Topics and Entities ingested into the Gen-AI Langchain application. For details on `Pebblo Daemon` see this [pebblo daemon](https://daxa-ai.github.io/pebblo-docs/daemon.html) document.\n",
-    "\n",
-    "Pebblo Safeloader enables safe data ingestion for Langchain `DocumentLoader`. This is done by wrapping the document loader call with `Pebblo Safe DocumentLoader`.\n",
-    "\n",
-    "#### How to Pebblo enable Document Loading?\n",
-    "\n",
-    "Assume a Langchain RAG application snippet using `CSVLoader` to read a CSV document for inference.\n",
-    "\n",
-    "Here is the snippet of Document loading using `CSVLoader`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.csv_loader import CSVLoader\n",
-    "\n",
-    "loader = CSVLoader(\"data/corp_sens_data.csv\")\n",
-    "documents = loader.load()\n",
-    "print(documents)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Pebblo SafeLoader can be enabled with few lines of code change to the above snippet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.csv_loader import CSVLoader\n",
-    "from langchain_community.document_loaders import PebbloSafeLoader\n",
-    "\n",
-    "loader = PebbloSafeLoader(\n",
-    "    CSVLoader(\"data/corp_sens_data.csv\"),\n",
-    "    name=\"acme-corp-rag-1\",  # App name (Mandatory)\n",
-    "    owner=\"Joe Smith\",  # Owner (Optional)\n",
-    "    description=\"Support productivity RAG application\",  # Description (Optional)\n",
-    ")\n",
-    "documents = loader.load()\n",
-    "print(documents)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/document_loaders/psychic.ipynb

@@ -8,7 +8,7 @@
     "This notebook covers how to load documents from `Psychic`. See [here](/docs/integrations/providers/psychic) for more details.\n",
     "\n",
     "## Prerequisites\n",
-    "1. Follow the Quick Start section in [this document](/docs/integrations/providers/psychic)\n",
+    "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic)\n",
     "2. Log into the [Psychic dashboard](https://dashboard.psychic.dev/) and get your secret key\n",
     "3. Install the frontend react library into your web app and have a user authenticate a connection. The connection will be created using the connection id that you specify."
    ]

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -13,16 +13,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install --upgrade --quiet  nest_asyncio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,11 +54,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")\n",
+    "sitemap_loader = SitemapLoader(web_path=\"https://langchain.readthedocs.io/sitemap.xml\")\n",
     "\n",
     "docs = sitemap_loader.load()"
    ]
@@ -79,7 +90,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-02-09T01:10:49.422114+00:00', 'changefreq': 'weekly', 'priority': '1'})"
+       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2023-10-13T18:13:26.966937+00:00', 'changefreq': 'weekly', 'priority': '1'})"
       ]
      },
      "execution_count": 6,
@@ -102,12 +113,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 16.39it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "loader = SitemapLoader(\n",
-    "    web_path=\" https://api.python.langchain.com/sitemap.xml\",\n",
+    "    web_path=\"https://langchain.readthedocs.io/sitemap.xml\",\n",
     "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
     ")\n",
     "documents = loader.load()"
@@ -115,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 28,
    "metadata": {
     "scrolled": true
    },
@@ -123,10 +142,10 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2023-10-13T18:09:58.478681+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,12 +211,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "loader = SitemapLoader(\n",
-    "    \"https://api.python.langchain.com/sitemap.xml\",\n",
+    "    \"https://langchain.readthedocs.io/sitemap.xml\",\n",
     "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
     "    parsing_function=remove_nav_and_header_elements,\n",
     ")"
@@ -214,9 +233,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 3/3 [00:00<00:00, 12.46it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
     "\n",

docs/docs/integrations/document_loaders/source_code.ipynb

@@ -9,35 +9,7 @@
     "\n",
     "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a separate document.\n",
     "\n",
-    "This approach can potentially improve the accuracy of QA models over source code.\n",
-    "\n",
-    "The supported languages for code parsing are:\n",
-    "\n",
-    "- C (*)\n",
-    "- C++ (*)\n",
-    "- C# (*)\n",
-    "- COBOL\n",
-    "- Go (*)\n",
-    "- Java (*)\n",
-    "- JavaScript (requires package `esprima`)\n",
-    "- Kotlin (*)\n",
-    "- Lua (*)\n",
-    "- Perl (*)\n",
-    "- Python\n",
-    "- Ruby (*)\n",
-    "- Rust (*)\n",
-    "- Scala (*)\n",
-    "- TypeScript (*)\n",
-    "\n",
-    "Items marked with (*) require the packages `tree_sitter` and `tree_sitter_languages`.\n",
-    "It is straightforward to add support for additional languages using `tree_sitter`,\n",
-    "although this currently requires modifying LangChain.\n",
-    "\n",
-    "The language used for parsing can be configured, along with the minimum number of\n",
-    "lines required to activate the splitting based on syntax.\n",
-    "\n",
-    "If a language is not explicitly specified, `LanguageParser` will infer one from\n",
-    "filename extensions, if present."
+    "This approach can potentially improve the accuracy of QA models over source code. Currently, the supported languages for code parsing are Python and JavaScript. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax."
    ]
   },
   {
@@ -47,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU esprima esprima tree_sitter tree_sitter_languages"
+    "%pip install --upgrade --quiet  esprima"
    ]
   },
   {
@@ -423,33 +395,6 @@
    "source": [
     "print(\"\\n\\n--8<--\\n\\n\".join([document.page_content for document in result]))"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding Languages using Tree-sitter Template\n",
-    "\n",
-    "Expanding language support using the Tree-Sitter template involves a few essential steps:\n",
-    "\n",
-    "1. **Creating a New Language File**:\n",
-    "    - Begin by creating a new file in the designated directory (langchain/libs/community/langchain_community/document_loaders/parsers/language).\n",
-    "    - Model this file based on the structure and parsing logic of existing language files like **`cpp.py`**.\n",
-    "    - You will also need to create a file in the langchain directory (langchain/libs/langchain/langchain/document_loaders/parsers/language).\n",
-    "2. **Parsing Language Specifics**:\n",
-    "    - Mimic the structure used in the **`cpp.py`** file, adapting it to suit the language you are incorporating.\n",
-    "    - The primary alteration involves adjusting the chunk query array to suit the syntax and structure of the language you are parsing.\n",
-    "3. **Testing the Language Parser**:\n",
-    "    - For thorough validation, generate a test file specific to the new language. Create **`test_language.py`** in the designated directory(langchain/libs/community/tests/unit_tests/document_loaders/parsers/language).\n",
-    "    - Follow the example set by **`test_cpp.py`** to establish fundamental tests for the parsed elements in the new language.\n",
-    "4. **Integration into the Parser and Text Splitter**:\n",
-    "    - Incorporate your new language within the **`language_parser.py`** file. Ensure to update LANGUAGE_EXTENSIONS and LANGUAGE_SEGMENTERS along with the docstring for LanguageParser to recognize and handle the added language.\n",
-    "    - Also, confirm that your language is included in **`text_splitter.py`** in class Language for proper parsing.\n",
-    "\n",
-    "By following these steps and ensuring comprehensive testing and integration, you'll successfully extend language support using the Tree-Sitter template.\n",
-    "\n",
-    "Best of luck!"
-   ]
   }
  ],
  "metadata": {
@@ -468,7 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/tomarkdown.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "497736aa",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "009e0036",
    "metadata": {},
    "outputs": [],
@@ -34,19 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "910fb6ee",
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = ToMarkdownLoader(\n",
-    "    url=\"https://python.langchain.com/docs/get_started/introduction\", api_key=api_key\n",
+    "loader = ToMarkdownLoader.from_api_key(\n",
+    "    url=\"https://python.langchain.com/en/latest/\", api_key=api_key\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "ac8db139",
    "metadata": {},
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "706304e9",
    "metadata": {},
    "outputs": [
@@ -64,106 +64,130 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "**LangChain** is a framework for developing applications powered by language models. It enables applications that:\n",
+      "## Contents\n",
       "\n",
-      "- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)\n",
-      "- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)\n",
+      "- [Getting Started](#getting-started)\n",
+      "- [Modules](#modules)\n",
+      "- [Use Cases](#use-cases)\n",
+      "- [Reference Docs](#reference-docs)\n",
+      "- [LangChain Ecosystem](#langchain-ecosystem)\n",
+      "- [Additional Resources](#additional-resources)\n",
       "\n",
-      "This framework consists of several parts.\n",
+      "## Welcome to LangChain [\\#](\\#welcome-to-langchain \"Permalink to this headline\")\n",
       "\n",
-      "- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.\n",
-      "- **[LangChain Templates](https://python.langchain.com/docs/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.\n",
-      "- **[LangServe](https://python.langchain.com/docs/langserve)**: A library for deploying LangChain chains as a REST API.\n",
-      "- **[LangSmith](https://python.langchain.com/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.\n",
+      "**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:\n",
       "\n",
-      "![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](https://python.langchain.com/assets/images/langchain_stack-f21828069f74484521f38199910007c1.svg)\n",
+      "1. _Data-aware_: connect a language model to other sources of data\n",
       "\n",
-      "Together, these products simplify the entire application lifecycle:\n",
+      "2. _Agentic_: allow a language model to interact with its environment\n",
       "\n",
-      "- **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.\n",
-      "- **Productionize**: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.\n",
-      "- **Deploy**: Turn any chain into an API with LangServe.\n",
       "\n",
-      "## LangChain Libraries [​](\\#langchain-libraries \"Direct link to LangChain Libraries\")\n",
+      "The LangChain framework is designed around these principles.\n",
       "\n",
-      "The main value props of the LangChain packages are:\n",
+      "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/).\n",
       "\n",
-      "1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not\n",
-      "2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks\n",
+      "## Getting Started [\\#](\\#getting-started \"Permalink to this headline\")\n",
       "\n",
-      "Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.\n",
+      "How to get started using LangChain to create an Language Model application.\n",
       "\n",
-      "The LangChain libraries themselves are made up of several different packages.\n",
+      "- [Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html)\n",
       "\n",
-      "- **`langchain-core`**: Base abstractions and LangChain Expression Language.\n",
-      "- **`langchain-community`**: Third party integrations.\n",
-      "- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.\n",
       "\n",
-      "## Get started [​](\\#get-started \"Direct link to Get started\")\n",
+      "Concepts and terminology.\n",
       "\n",
-      "[Here’s](https://python.langchain.com/docs/get_started/installation) how to install LangChain, set up your environment, and start building.\n",
+      "- [Concepts and terminology](https://python.langchain.com/en/latest/getting_started/concepts.html)\n",
       "\n",
-      "We recommend following our [Quickstart](https://python.langchain.com/docs/get_started/quickstart) guide to familiarize yourself with the framework by building your first LangChain application.\n",
       "\n",
-      "Read up on our [Security](https://python.langchain.com/docs/security) best practices to make sure you're developing safely with LangChain.\n",
+      "Tutorials created by community experts and presented on YouTube.\n",
       "\n",
-      "note\n",
+      "- [Tutorials](https://python.langchain.com/en/latest/getting_started/tutorials.html)\n",
       "\n",
-      "These docs focus on the Python LangChain library. [Head here](https://js.langchain.com) for docs on the JavaScript LangChain library.\n",
       "\n",
-      "## LangChain Expression Language (LCEL) [​](\\#langchain-expression-language-lcel \"Direct link to LangChain Expression Language (LCEL)\")\n",
+      "## Modules [\\#](\\#modules \"Permalink to this headline\")\n",
       "\n",
-      "LCEL is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.\n",
+      "These modules are the core abstractions which we view as the building blocks of any LLM-powered application.\n",
       "\n",
-      "- **[Overview](https://python.langchain.com/docs/expression_language/)**: LCEL and its benefits\n",
-      "- **[Interface](https://python.langchain.com/docs/expression_language/interface)**: The standard interface for LCEL objects\n",
-      "- **[How-to](https://python.langchain.com/docs/expression_language/how_to)**: Key features of LCEL\n",
-      "- **[Cookbook](https://python.langchain.com/docs/expression_language/cookbook)**: Example code for accomplishing common tasks\n",
+      "For each module LangChain provides standard, extendable interfaces. LanghChain also provides external integrations and even end-to-end implementations for off-the-shelf use.\n",
       "\n",
-      "## Modules [​](\\#modules \"Direct link to Modules\")\n",
+      "The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.\n",
       "\n",
-      "LangChain provides standard, extendable interfaces and integrations for the following modules:\n",
+      "The modules are (from least to most complex):\n",
       "\n",
-      "#### [Model I/O](https://python.langchain.com/docs/modules/model_io/) [​](\\#model-io \"Direct link to model-io\")\n",
+      "- [Models](https://python.langchain.com/docs/modules/model_io/models/): Supported model types and integrations.\n",
       "\n",
-      "Interface with language models\n",
+      "- [Prompts](https://python.langchain.com/en/latest/modules/prompts.html): Prompt management, optimization, and serialization.\n",
       "\n",
-      "#### [Retrieval](https://python.langchain.com/docs/modules/data_connection/) [​](\\#retrieval \"Direct link to retrieval\")\n",
+      "- [Memory](https://python.langchain.com/en/latest/modules/memory.html): Memory refers to state that is persisted between calls of a chain/agent.\n",
       "\n",
-      "Interface with application-specific data\n",
+      "- [Indexes](https://python.langchain.com/en/latest/modules/data_connection.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.\n",
       "\n",
-      "#### [Agents](https://python.langchain.com/docs/modules/agents/) [​](\\#agents \"Direct link to agents\")\n",
+      "- [Chains](https://python.langchain.com/en/latest/modules/chains.html): Chains are structured sequences of calls (to an LLM or to a different utility).\n",
       "\n",
-      "Let models choose which tools to use given high-level directives\n",
+      "- [Agents](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "## Examples, ecosystem, and resources [​](\\#examples-ecosystem-and-resources \"Direct link to Examples, ecosystem, and resources\")\n",
+      "- [Callbacks](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "### [Use cases](https://python.langchain.com/docs/use_cases/question_answering/) [​](\\#use-cases \"Direct link to use-cases\")\n",
       "\n",
-      "Walkthroughs and techniques for common end-to-end use cases, like:\n",
+      "## Use Cases [\\#](\\#use-cases \"Permalink to this headline\")\n",
       "\n",
-      "- [Document question answering](https://python.langchain.com/docs/use_cases/question_answering/)\n",
-      "- [Chatbots](https://python.langchain.com/docs/use_cases/chatbots/)\n",
-      "- [Analyzing structured data](https://python.langchain.com/docs/use_cases/sql/)\n",
-      "- and much more...\n",
+      "Best practices and built-in implementations for common LangChain use cases:\n",
       "\n",
-      "### [Integrations](https://python.langchain.com/docs/integrations/providers/) [​](\\#integrations \"Direct link to integrations\")\n",
+      "- [Autonomous Agents](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](https://python.langchain.com/docs/integrations/providers/).\n",
+      "- [Agent Simulations](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "### [Guides](https://python.langchain.com/docs/guides/debugging) [​](\\#guides \"Direct link to guides\")\n",
+      "- [Personal Assistants](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "Best practices for developing with LangChain.\n",
+      "- [Question Answering](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "### [API reference](https://api.python.langchain.com) [​](\\#api-reference \"Direct link to api-reference\")\n",
+      "- [Chatbots](https://python.langchain.com/en/latest/use_cases/chatbots.html): Language models love to chat, making this a very natural use of them.\n",
       "\n",
-      "Head to the reference section for full documentation of all classes and methods in the LangChain and LangChain Experimental Python packages.\n",
+      "- [Querying Tabular Data](https://python.langchain.com/en/latest/use_cases/tabular.html): Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).\n",
       "\n",
-      "### [Developer's guide](https://python.langchain.com/docs/contributing) [​](\\#developers-guide \"Direct link to developers-guide\")\n",
+      "- [Code Understanding](https://python.langchain.com/en/latest/use_cases/code.html): Recommended reading if you want to use language models to analyze code.\n",
       "\n",
-      "Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.\n",
+      "- [Interacting with APIs](https://python.langchain.com/en/latest/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.\n",
       "\n",
-      "Head to the [Community navigator](https://python.langchain.com/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.\n"
+      "- [Extraction](https://python.langchain.com/en/latest/use_cases/extraction.html): Extract structured information from text.\n",
+      "\n",
+      "- [Summarization](https://python.langchain.com/en/latest/use_cases/summarization.html): Compressing longer documents. A type of Data-Augmented Generation.\n",
+      "\n",
+      "- [Evaluation](https://python.langchain.com/en/latest/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.\n",
+      "\n",
+      "\n",
+      "## Reference Docs [\\#](\\#reference-docs \"Permalink to this headline\")\n",
+      "\n",
+      "Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n",
+      "\n",
+      "- [Reference Documentation](https://python.langchain.com/en/latest/reference.html)\n",
+      "\n",
+      "\n",
+      "## LangChain Ecosystem [\\#](\\#langchain-ecosystem \"Permalink to this headline\")\n",
+      "\n",
+      "Guides for how other companies/products can be used with LangChain.\n",
+      "\n",
+      "- [LangChain Ecosystem](https://python.langchain.com/en/latest/ecosystem.html)\n",
+      "\n",
+      "\n",
+      "## Additional Resources [\\#](\\#additional-resources \"Permalink to this headline\")\n",
+      "\n",
+      "Additional resources we think may be useful as you develop your application!\n",
+      "\n",
+      "- [LangChainHub](https://github.com/hwchase17/langchain-hub): The LangChainHub is a place to share and explore other prompts, chains, and agents.\n",
+      "\n",
+      "- [Gallery](https://python.langchain.com/en/latest/additional_resources/gallery.html): A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\n",
+      "\n",
+      "- [Deployments](https://python.langchain.com/en/latest/additional_resources/deployments.html): A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\n",
+      "\n",
+      "- [Tracing](https://python.langchain.com/en/latest/additional_resources/tracing.html): A guide on using tracing in LangChain to visualize the execution of chains and agents.\n",
+      "\n",
+      "- [Model Laboratory](https://python.langchain.com/en/latest/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.\n",
+      "\n",
+      "- [Discord](https://discord.gg/6adMQxSpJS): Join us on our Discord to discuss all things LangChain!\n",
+      "\n",
+      "- [YouTube](https://python.langchain.com/en/latest/additional_resources/youtube.html): A collection of the LangChain tutorials and videos.\n",
+      "\n",
+      "- [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.\n"
      ]
     }
    ],
@@ -174,7 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c89b313-adb6-4aa2-9cd8-952a5724a2ce",
+   "id": "5dde17e7",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -196,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,