core[patch]: only support pydantic >= 2.9 for now (#26176)

For now we'll only support pydantic ^ 2.9. We'll relax the constraint
next week once we work around some issues with pydantic 2.7 / 2.8.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -96,21 +96,25 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us. Do NOT skip this step and please don't trim
-        the output. Most users don't include enough information here and it makes it harder
-        for us to help you.
+        Please share your system info with us. 
         
-        Run the following command in your terminal and paste the output here:
+        "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
-        
-        or if you have an existing python interpreter running:
-        
-        from langchain_core import sys_info
-        sys_info.print_sys_info()
-        
-        alternatively, put the entire output of `pip freeze` here.
       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/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,7 @@
 Thank you for contributing to LangChain!
 
 - [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
+  - 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"
 
 

.github/scripts/check_diff.py

@@ -2,12 +2,10 @@ import glob
 import json
 import os
 import sys
+import tomllib
 from collections import defaultdict
 from typing import Dict, List, Set
 from pathlib import Path
-import tomllib
-
-from get_min_versions import get_min_version_from_toml
 
 
 LANGCHAIN_DIRS = [
@@ -15,13 +13,12 @@ LANGCHAIN_DIRS = [
     "libs/text-splitters",
     "libs/langchain",
     "libs/community",
+    "libs/experimental",
 ]
 
-# when set to True, we are ignoring core dependents
-# in order to be able to get CI to pass for each individual
-# package that depends on core
-# e.g. if you touch core, we don't then add textsplitters/etc to CI
-IGNORE_CORE_DEPENDENTS = False
+# for 0.3rc, we are ignoring core dependents
+# in order to be able to get CI to pass for individual PRs.
+IGNORE_CORE_DEPENDENTS = True
 
 # ignored partners are removed from dependents
 # but still run if directly edited
@@ -106,101 +103,44 @@ def add_dependents(dirs_to_eval: Set[str], dependents: dict) -> List[str]:
 
 
 def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
-    if job == "test-pydantic":
-        return _get_pydantic_test_configs(dir_)
-
     if dir_ == "libs/core":
-        py_versions = ["3.9", "3.10", "3.11", "3.12"]
+        return [
+            {"working-directory": dir_, "python-version": f"3.{v}"}
+            for v in range(9, 13)
+        ]
+    min_python = "3.9"
+    max_python = "3.12"
+
     # custom logic for specific directories
-    elif dir_ == "libs/partners/milvus":
+    if dir_ == "libs/partners/milvus":
         # milvus poetry doesn't allow 3.12 because they
         # declare deps in funny way
-        py_versions = ["3.9", "3.11"]
+        max_python = "3.11"
 
-    elif dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
+    if dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
         # community extended test resolution in 3.12 is slow
         # even in uv
-        py_versions = ["3.9", "3.11"]
+        max_python = "3.11"
 
-    elif dir_ == "libs/community" and job == "compile-integration-tests":
+    if dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
-        py_versions = ["3.9", "3.11"]
-    else:
-        py_versions = ["3.9", "3.12"]
+        max_python = "3.11"
 
-    return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
-
-
-def _get_pydantic_test_configs(
-    dir_: str, *, python_version: str = "3.11"
-) -> List[Dict[str, str]]:
-    with open("./libs/core/poetry.lock", "rb") as f:
-        core_poetry_lock_data = tomllib.load(f)
-    for package in core_poetry_lock_data["package"]:
-        if package["name"] == "pydantic":
-            core_max_pydantic_minor = package["version"].split(".")[1]
-            break
-
-    with open(f"./{dir_}/poetry.lock", "rb") as f:
-        dir_poetry_lock_data = tomllib.load(f)
-
-    for package in dir_poetry_lock_data["package"]:
-        if package["name"] == "pydantic":
-            dir_max_pydantic_minor = package["version"].split(".")[1]
-            break
-
-    core_min_pydantic_version = get_min_version_from_toml(
-        "./libs/core/pyproject.toml", "release", python_version, include=["pydantic"]
-    )["pydantic"]
-    core_min_pydantic_minor = (
-        core_min_pydantic_version.split(".")[1]
-        if "." in core_min_pydantic_version
-        else "0"
-    )
-    dir_min_pydantic_version = get_min_version_from_toml(
-        f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
-    ).get("pydantic", "0.0.0")
-    dir_min_pydantic_minor = (
-        dir_min_pydantic_version.split(".")[1]
-        if "." in dir_min_pydantic_version
-        else "0"
-    )
-
-    custom_mins = {
-        # depends on pydantic-settings 2.4 which requires pydantic 2.7
-        "libs/community": 7,
-    }
-
-    max_pydantic_minor = min(
-        int(dir_max_pydantic_minor),
-        int(core_max_pydantic_minor),
-    )
-    min_pydantic_minor = max(
-        int(dir_min_pydantic_minor),
-        int(core_min_pydantic_minor),
-        custom_mins.get(dir_, 0),
-    )
-
-    configs = [
-        {
-            "working-directory": dir_,
-            "pydantic-version": f"2.{v}.0",
-            "python-version": python_version,
-        }
-        for v in range(min_pydantic_minor, max_pydantic_minor + 1)
+    return [
+        {"working-directory": dir_, "python-version": min_python},
+        {"working-directory": dir_, "python-version": max_python},
     ]
-    return configs
 
 
 def _get_configs_for_multi_dirs(
-    job: str, dirs_to_run: Dict[str, Set[str]], dependents: dict
+    job: str, dirs_to_run: List[str], dependents: dict
 ) -> List[Dict[str, str]]:
     if job == "lint":
         dirs = add_dependents(
             dirs_to_run["lint"] | dirs_to_run["test"] | dirs_to_run["extended-test"],
             dependents,
         )
-    elif job in ["test", "compile-integration-tests", "dependencies", "test-pydantic"]:
+    elif job in ["test", "compile-integration-tests", "dependencies"]:
         dirs = add_dependents(
             dirs_to_run["test"] | dirs_to_run["extended-test"], dependents
         )
@@ -229,7 +169,6 @@ if __name__ == "__main__":
         dirs_to_run["lint"] = all_package_dirs()
         dirs_to_run["test"] = all_package_dirs()
         dirs_to_run["extended-test"] = set(LANGCHAIN_DIRS)
-
     for file in files:
         if any(
             file.startswith(dir_)
@@ -247,7 +186,6 @@ if __name__ == "__main__":
         if any(file.startswith(dir_) for dir_ in LANGCHAIN_DIRS):
             # add that dir and all dirs after in LANGCHAIN_DIRS
             # for extended testing
-
             found = False
             for dir_ in LANGCHAIN_DIRS:
                 if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS:
@@ -293,6 +231,7 @@ if __name__ == "__main__":
 
     # we now have dirs_by_job
     # todo: clean this up
+
     map_job_to_configs = {
         job: _get_configs_for_multi_dirs(job, dirs_to_run, dependents)
         for job in [
@@ -301,7 +240,6 @@ if __name__ == "__main__":
             "extended-tests",
             "compile-integration-tests",
             "dependencies",
-            "test-pydantic",
         ]
     }
     map_job_to_configs["test-doc-imports"] = (

.github/scripts/get_min_versions.py

@@ -1,5 +1,4 @@
 import sys
-from typing import Optional
 
 if sys.version_info >= (3, 11):
     import tomllib
@@ -8,9 +7,6 @@ else:
     import tomli as tomllib
 
 from packaging.version import parse as parse_version
-from packaging.specifiers import SpecifierSet
-from packaging.version import Version
-
 import re
 
 MIN_VERSION_LIBS = [
@@ -19,16 +15,10 @@ MIN_VERSION_LIBS = [
     "langchain",
     "langchain-text-splitters",
     "SQLAlchemy",
+    "pydantic",
 ]
 
-# some libs only get checked on release because of simultaneous changes in
-# multiple libs
-SKIP_IF_PULL_REQUEST = [
-    "langchain-core",
-    "langchain-text-splitters",
-    "langchain",
-    "langchain-community",
-]
+SKIP_IF_PULL_REQUEST = ["langchain-core"]
 
 
 def get_min_version(version: str) -> str:
@@ -56,13 +46,7 @@ def get_min_version(version: str) -> str:
     raise ValueError(f"Unrecognized version format: {version}")
 
 
-def get_min_version_from_toml(
-    toml_path: str,
-    versions_for: str,
-    python_version: str,
-    *,
-    include: Optional[list] = None,
-):
+def get_min_version_from_toml(toml_path: str, versions_for: str):
     # Parse the TOML file
     with open(toml_path, "rb") as file:
         toml_data = tomllib.load(file)
@@ -74,26 +58,18 @@ def get_min_version_from_toml(
     min_versions = {}
 
     # Iterate over the libs in MIN_VERSION_LIBS
-    for lib in set(MIN_VERSION_LIBS + (include or [])):
+    for lib in MIN_VERSION_LIBS:
         if versions_for == "pull_request" and lib in SKIP_IF_PULL_REQUEST:
             # some libs only get checked on release because of simultaneous
-            # changes in multiple libs
+            # changes
             continue
         # Check if the lib is present in the dependencies
         if lib in dependencies:
-            if include and lib not in include:
-                continue
             # Get the version string
             version_string = dependencies[lib]
 
             if isinstance(version_string, dict):
                 version_string = version_string["version"]
-            if isinstance(version_string, list):
-                version_string = [
-                    vs
-                    for vs in version_string
-                    if check_python_version(python_version, vs["python"])
-                ][0]["version"]
 
             # Use parse_version to get the minimum supported version from version_string
             min_version = get_min_version(version_string)
@@ -104,31 +80,13 @@ def get_min_version_from_toml(
     return min_versions
 
 
-def check_python_version(version_string, constraint_string):
-    """
-    Check if the given Python version matches the given constraints.
-
-    :param version_string: A string representing the Python version (e.g. "3.8.5").
-    :param constraint_string: A string representing the package's Python version constraints (e.g. ">=3.6, <4.0").
-    :return: True if the version matches the constraints, False otherwise.
-    """
-    try:
-        version = Version(version_string)
-        constraints = SpecifierSet(constraint_string)
-        return version in constraints
-    except Exception as e:
-        print(f"Error: {e}")
-        return False
-
-
 if __name__ == "__main__":
     # Get the TOML file path from the command line argument
     toml_file = sys.argv[1]
     versions_for = sys.argv[2]
-    python_version = sys.argv[3]
     assert versions_for in ["release", "pull_request"]
 
     # Call the function to get the minimum versions
-    min_versions = get_min_version_from_toml(toml_file, versions_for, python_version)
+    min_versions = get_min_version_from_toml(toml_file, versions_for)
 
     print(" ".join([f"{lib}=={version}" for lib, version in min_versions.items()]))

.github/workflows/_integration_test.yml

@@ -58,7 +58,6 @@ jobs:
           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_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_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 }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
@@ -68,7 +67,6 @@ jobs:
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/_lint.yml

@@ -7,6 +7,10 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+      langchain-location:
+        required: false
+        type: string
+        description: "Relative path to the langchain library folder"
       python-version:
         required: true
         type: string
@@ -59,6 +63,14 @@ jobs:
         run: |
           poetry install --with lint,typing
 
+      - name: Install langchain editable
+        working-directory: ${{ inputs.working-directory }}
+        if: ${{ inputs.langchain-location }}
+        env:
+          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
+        run: |
+          poetry run pip install -e "$LANGCHAIN_LOCATION"
+
       - name: Get .mypy_cache to speed up mypy
         uses: actions/cache@v4
         env:

.github/workflows/_release.yml

@@ -85,7 +85,7 @@ jobs:
           path: langchain
           sparse-checkout: | # this only grabs files for relevant dir
             ${{ inputs.working-directory }}
-          ref: ${{ github.ref }} # this scopes to just ref'd branch
+          ref: master # this scopes to just master branch
           fetch-depth: 0 # this fetches entire commit history
       - name: Check Tags
         id: check-tags
@@ -164,7 +164,6 @@ jobs:
 
       - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
-        id: setup-python
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
@@ -232,8 +231,7 @@ jobs:
         id: min-version
         run: |
           poetry run pip install packaging
-          python_version="$(poetry run python --version | awk '{print $2}')"
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release $python_version)"
+          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release)"
           echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
           echo "min-versions=$min_versions"
 
@@ -269,14 +267,12 @@ jobs:
           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_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_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 }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
@@ -294,6 +290,7 @@ jobs:
           VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
+          UNSTRUCTURED_API_KEY: ${{ secrets.UNSTRUCTURED_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/_test.yml

@@ -7,6 +7,10 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+      langchain-location:
+        required: false
+        type: string
+        description: "Relative path to the langchain library folder"
       python-version:
         required: true
         type: string
@@ -27,41 +31,29 @@ jobs:
 
       - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
-        id: setup-python
         with:
           python-version: ${{ inputs.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: core
+
       - name: Install dependencies
         shell: bash
         run: poetry install --with test
 
+      - name: Install langchain editable
+        working-directory: ${{ inputs.working-directory }}
+        if: ${{ inputs.langchain-location }}
+        env:
+          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
+        run: |
+          poetry run pip install -e "$LANGCHAIN_LOCATION"
+
       - name: Run core tests
         shell: bash
         run: |
           make test
 
-      - name: Get minimum versions
-        working-directory: ${{ inputs.working-directory }}
-        id: min-version
-        shell: bash
-        run: |
-          poetry run pip install packaging tomli
-          python_version="$(poetry run python --version | awk '{print $2}')"
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml pull_request $python_version)"
-          echo "min-versions=$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 }}
-        run: |
-          poetry run pip install $MIN_VERSIONS
-          make tests
-        working-directory: ${{ inputs.working-directory }}
-
       - name: Ensure the tests did not create any additional files
         shell: bash
         run: |
@@ -74,3 +66,20 @@ jobs:
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
           
+      - name: Get minimum versions
+        working-directory: ${{ inputs.working-directory }}
+        id: min-version
+        run: |
+          poetry run pip install packaging tomli
+          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml pull_request)"
+          echo "min-versions=$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 }}
+        run: |
+          poetry run pip install --force-reinstall $MIN_VERSIONS --editable .
+          make tests
+        working-directory: ${{ inputs.working-directory }}

.github/workflows/_test_doc_imports.yml

@@ -31,7 +31,7 @@ jobs:
 
       - name: Install langchain editable
         run: |
-          poetry run pip install langchain-experimental -e libs/core libs/langchain libs/community
+          poetry run pip install -e libs/core libs/langchain libs/community libs/experimental
 
       - name: Check doc imports
         shell: bash

.github/workflows/_test_pydantic.yml

@@ -1,64 +0,0 @@
-name: test pydantic intermediate versions
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      python-version:
-        required: false
-        type: string
-        description: "Python version to use"
-        default: "3.11"
-      pydantic-version:
-        required: true
-        type: string
-        description: "Pydantic version to test."
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    name: "make test # pydantic: ~=${{ inputs.pydantic-version }}, python: ${{ inputs.python-version }}, "
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: core
-
-      - name: Install dependencies
-        shell: bash
-        run: poetry install --with test
-
-      - name: Overwrite pydantic version
-        shell: bash
-        run: poetry run pip install pydantic~=${{ inputs.pydantic-version }}
-
-      - name: Run core tests
-        shell: bash
-        run: |
-          make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/api_doc_build.yml

@@ -1,153 +0,0 @@
-name: API docs build
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron:  '0 13 * * *'
-env:
-  POETRY_VERSION: "1.8.1"
-  PYTHON_VERSION: "3.11"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    permissions: write-all
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          path: langchain
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-api-docs-html
-          path: langchain-api-docs-html
-          token: ${{ secrets.TOKEN_GITHUB_API_DOCS_HTML }}
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-google
-          path: langchain-google
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-datastax
-          path: langchain-datastax
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-nvidia
-          path: langchain-nvidia
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-cohere
-          path: langchain-cohere
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-elastic
-          path: langchain-elastic
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-postgres
-          path: langchain-postgres
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-aws
-          path: langchain-aws
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-weaviate
-          path: langchain-weaviate
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-ai21
-          path: langchain-ai21
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-together
-          path: langchain-together
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-experimental
-          path: langchain-experimental
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-milvus
-          path: langchain-milvus
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-unstructured
-          path: langchain-unstructured
-
-
-      - name: Set Git config
-        working-directory: langchain
-        run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "Github Actions"
-          
-      - name: Move libs
-        run: |
-          rm -rf \
-            langchain/libs/partners/google-genai \
-            langchain/libs/partners/google-vertexai \
-            langchain/libs/partners/astradb \
-            langchain/libs/partners/nvidia-trt \
-            langchain/libs/partners/nvidia-ai-endpoints \
-            langchain/libs/partners/cohere \
-            langchain/libs/partners/elasticsearch \
-            langchain/libs/partners/upstage \
-            langchain/libs/partners/ai21 \
-            langchain/libs/partners/together \
-            langchain/libs/standard-tests \
-            langchain/libs/experimental \
-            langchain/libs/partners/milvus \
-            langchain/libs/partners/unstructured
-          mv langchain-google/libs/genai langchain/libs/partners/google-genai
-          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
-          mv langchain-google/libs/community langchain/libs/partners/google-community
-          # mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
-          # mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
-          mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
-          mv langchain-elastic/libs/elasticsearch langchain/libs/partners/elasticsearch
-          mv langchain-postgres langchain/libs/partners/postgres
-          mv langchain-aws/libs/aws langchain/libs/partners/aws
-          mv langchain-weaviate/libs/weaviate langchain/libs/partners/weaviate
-          mv langchain-ai21/libs/ai21 langchain/libs/partners/ai21
-          mv langchain-together/libs/together langchain/libs/partners/together
-          mv langchain-experimental/libs/experimental langchain/libs/experimental
-          mv langchain-milvus/libs/milvus langchain/libs/partners/milvus
-          mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
-
-      - name: Rm old html
-        run:
-          rm -rf langchain-api-docs-html/api_reference_build/html
-
-      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./langchain/.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: api-docs
-          working-directory: langchain
-
-      - name: Install dependencies
-        working-directory: langchain
-        run: |
-          python -m pip install -U uv
-          python -m uv pip install --upgrade --no-cache-dir pip setuptools
-          # skip airbyte and ibm due to pandas dependency issue
-          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
-          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
-          python -m uv pip install -r docs/api_reference/requirements.txt
-
-      - name: Build docs
-        working-directory: langchain
-        run: |
-          python docs/api_reference/create_api_rst.py
-          python -m sphinx -T -E -b html -d ../langchain-api-docs-html/_build/doctrees -c docs/api_reference docs/api_reference ../langchain-api-docs-html/api_reference_build/html -j auto
-          python docs/api_reference/scripts/custom_formatter.py ../langchain-api-docs-html/api_reference_build/html
-          # Default index page is blank so we copy in the actual home page.
-          cp ../langchain-api-docs-html/api_reference_build/html/{reference,index}.html
-          rm -rf ../langchain-api-docs-html/_build/
-
-      # https://github.com/marketplace/actions/add-commit
-      - uses: EndBug/add-and-commit@v9
-        with:
-          cwd: langchain-api-docs-html
-          message: 'Update API docs build'

.github/workflows/check_diffs.yml

@@ -31,7 +31,6 @@ jobs:
         uses: Ana06/get-changed-files@v2.2.0
       - id: set-matrix
         run: |
-          python -m pip install packaging
           python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
       lint: ${{ steps.set-matrix.outputs.lint }}
@@ -40,7 +39,6 @@ jobs:
       compile-integration-tests: ${{ steps.set-matrix.outputs.compile-integration-tests }}
       dependencies: ${{ steps.set-matrix.outputs.dependencies }}
       test-doc-imports: ${{ steps.set-matrix.outputs.test-doc-imports }}
-      test-pydantic: ${{ steps.set-matrix.outputs.test-pydantic }}
   lint:
     name: cd ${{ matrix.job-configs.working-directory }}
     needs: [ build ]
@@ -69,20 +67,6 @@ jobs:
       python-version: ${{ matrix.job-configs.python-version }}
     secrets: inherit
 
-  test-pydantic:
-    name: cd ${{ matrix.job-configs.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.test-pydantic != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.test-pydantic) }}
-      fail-fast: false
-    uses: ./.github/workflows/_test_pydantic.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      pydantic-version: ${{ matrix.job-configs.pydantic-version }}
-    secrets: inherit
-
   test-doc-imports:
     needs: [ build ]
     if: ${{ needs.build.outputs.test-doc-imports != '[]' }}
@@ -157,7 +141,7 @@ jobs:
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
   ci_success:
     name: "CI Success"
-    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports, test-pydantic]
+    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/codespell.yml

@@ -3,8 +3,9 @@ name: CI / cd . / make spell_check
 
 on:
   push:
-    branches: [master, v0.1, v0.2]
+    branches: [master, v0.1]
   pull_request:
+    branches: [master, v0.1]
 
 permissions:
   contents: read

.github/workflows/run_notebooks.yml

@@ -1,63 +0,0 @@
-name: Run notebooks
-
-on:
-  workflow_dispatch:
-    inputs:
-      python_version:
-        description: 'Python version'
-        required: false
-        default: '3.11'
-      working-directory:
-        description: 'Working directory or subset (e.g., docs/docs/tutorials/llm_chain.ipynb)'
-        required: false
-        default: 'all'
-  schedule:
-    - cron: '0 13 * * *'
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    name: "Test docs"
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ github.event.inputs.python_version || '3.11' }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: run-notebooks
-
-      - name: Install dependencies
-        run: |
-          pip install -e libs/core
-          pip install -e libs/langchain
-          pip install -e libs/community
-          pip install --upgrade langchain-experimental
-          pip install -e libs//partners/anthropic
-          pip install -e libs//partners/chroma
-          pip install -e libs//partners/openai
-          pip install -e libs//partners/mistralai
-          pip install jupyter langgraph click pypdf vcrpy
-
-      - name: Pre-download tiktoken files
-        run: |
-          python docs/scripts/download_tiktoken.py
-
-      - name: Prepare notebooks
-        run: |
-          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
-
-      - name: Run notebooks
-        env:
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
-        run: |
-          ./docs/scripts/execute_notebooks.sh ${{ github.event.inputs.working-directory || 'all' }}

.github/workflows/scheduled_test.yml

@@ -86,12 +86,10 @@ jobs:
           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_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_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 }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}

Makefile

@@ -36,6 +36,7 @@ api_docs_build:
 API_PKG ?= text-splitters
 
 api_docs_quick_preview:
+	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
 	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/

README.md

@@ -38,8 +38,8 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/platforms/).
-  Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
+- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
+  Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
 
@@ -49,7 +49,7 @@ For these applications, LangChain simplifies the entire application lifecycle:
 - **`langchain-community`**: Third party integrations.
   - Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
+- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 
 ### Productionization:
 
@@ -65,20 +65,20 @@ For these applications, LangChain simplifies the entire application lifecycle:
 
 **❓ Question answering with RAG**
 
-- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
+- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/rag/)
 - End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
 
 **🧱 Extracting structured output**
 
-- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
+- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/extraction/)
 - End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
 
 **🤖 Chatbots**
 
-- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
+- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/chatbot/)
 - End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
 
-And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
+And much more! Head to the [Tutorials](https://python.langchain.com/v0.2/docs/tutorials/) section of the docs for more.
 
 ## 🚀 How does LangChain help?
 
@@ -93,10 +93,10 @@ Off-the-shelf chains make it easy to get started. Components make it easy to cus
 
 LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
 
-- **[Overview](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
-- **[Interface](https://python.langchain.com/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
-- **[Primitives](https://python.langchain.com/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
-- **[Cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
+- **[Overview](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
+- **[Interface](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
+- **[Primitives](https://python.langchain.com/v0.2/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
+- **[Cheatsheet](https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
 
 ## Components
 
@@ -104,24 +104,24 @@ Components fall into the following **modules**:
 
 **📃 Model I/O**
 
-This includes [prompt management](https://python.langchain.com/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/docs/concepts/#output-parsers).
+This includes [prompt management](https://python.langchain.com/v0.2/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/v0.2/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/v0.2/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/v0.2/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/v0.2/docs/concepts/#output-parsers).
 
 **📚 Retrieval**
 
-Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/#retrievers) it for use in the generation step.
+Retrieval Augmented Generation involves [loading data](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/v0.2/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/v0.2/docs/concepts/#retrievers) it for use in the generation step.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
 
 ## 📖 Documentation
 
 Please see [here](https://python.langchain.com) for full documentation, which includes:
 
-- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
+- [Introduction](https://python.langchain.com/v0.2/docs/introduction/): Overview of the framework and the structure of the docs.
 - [Tutorials](https://python.langchain.com/docs/use_cases/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
-- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
-- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
+- [How-to guides](https://python.langchain.com/v0.2/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
+- [Conceptual guide](https://python.langchain.com/v0.2/docs/concepts/): Conceptual explanations of the key parts of the framework.
 - [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.
 
 ## 🌐 Ecosystem
@@ -134,7 +134,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
-For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
+For detailed information on how to contribute, see [here](https://python.langchain.com/v0.2/docs/contributing/).
 
 ## 🌟 Contributors
 

cookbook/code-analysis-deeplake.ipynb

@@ -90,8 +90,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "# Please manually enter OpenAI Key"
    ]
   },

docs/Makefile

@@ -33,8 +33,8 @@ install-py-deps:
 	python3 -m venv .venv
 	$(PYTHON) -m pip install --upgrade pip
 	$(PYTHON) -m pip install --upgrade uv
-	$(PYTHON) -m uv pip install --pre -r vercel_requirements.txt
-	$(PYTHON) -m uv pip install --pre --editable $(PARTNER_DEPS_LIST)
+	$(PYTHON) -m uv pip install -r vercel_requirements.txt
+	$(PYTHON) -m uv pip install --editable $(PARTNER_DEPS_LIST)
 
 generate-files:
 	mkdir -p $(INTERMEDIATE_DIR)
@@ -46,7 +46,7 @@ generate-files:
 
 	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
 
-	curl https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md | sed 's/<=/\&lt;=/g' > $(INTERMEDIATE_DIR)/langserve.md
+	wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O $(INTERMEDIATE_DIR)/langserve.md
 	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langserve.md https://github.com/langchain-ai/langserve/tree/main/
 
 copy-infra:
@@ -65,7 +65,7 @@ render:
 	$(PYTHON) scripts/notebook_convert.py $(INTERMEDIATE_DIR) $(OUTPUT_NEW_DOCS_DIR)
 
 md-sync:
-	rsync -avmq --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
+	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
 append-related:
 	$(PYTHON) scripts/append_related_links.py $(OUTPUT_NEW_DOCS_DIR)
@@ -82,10 +82,14 @@ vercel-build: install-vercel-deps build generate-references
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
 	mkdir static/api_reference
-	git clone --depth=1 https://github.com/langchain-ai/langchain-api-docs-html.git
-	mv langchain-api-docs-html/api_reference_build/html/* static/api_reference/
-	rm -rf langchain-api-docs-html
+	git clone --depth=1 https://github.com/baskaryan/langchain-api-docs-build.git
+	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
+	rm -rf langchain-api-docs-build
 	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
+	mv build v0.2
+	mkdir build
+	mv v0.2 build
+	mv build/v0.2/404.html build
 
 start:
 	cd $(OUTPUT_NEW_DIR) && yarn && yarn start --port=$(PORT)

docs/api_reference/conf.py

@@ -26,6 +26,7 @@ from sphinx.util.docutils import SphinxDirective
 _DIR = Path(__file__).parent.absolute()
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath("../../libs/langchain"))
+sys.path.insert(0, os.path.abspath("../../libs/experimental"))
 
 with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
     data = toml.load(f)

docs/api_reference/requirements.txt

@@ -1,5 +1,5 @@
-autodoc_pydantic>=2,<3
-sphinx>=8,<9
+autodoc_pydantic>=1,<2
+sphinx<=7
 myst-parser>=3
 sphinx-autobuild>=2024
 pydata-sphinx-theme>=0.15
@@ -8,4 +8,4 @@ myst-nb>=1.1.1
 pyyaml
 sphinx-design
 sphinx-copybutton
-beautifulsoup4
+beautifulsoup4

docs/api_reference/scripts/custom_formatter.py

@@ -17,10 +17,7 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        try:
-            element = element.a.code.span
-        except Exception:
-            continue
+        element = element.a.code.span
         # Get the text content of the element
         content = element.get_text()
 

docs/api_reference/templates/pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, model_extra, model_fields_set, model_json_schema
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace
 
 
     {% block attributes %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema, predict, apredict, predict_messages, apredict_messages, generate, generate_prompt, agenerate, agenerate_prompt, call_as_llm
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 

docs/cassettes/agents_114ba50d.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/agents_24460239.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/agents_482ce13d.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/agents_532d6557.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/agents_56d8028b-5dbc-40b2-86f5-ed60631d86a3.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/agents_688b465d.msgpack.zlib

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docs/cassettes/agents_77c2f769.msgpack.zlib

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docs/cassettes/agents_a13462d0-2d02-4474-921e-15a1ba1fa274.msgpack.zlib

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docs/cassettes/agents_a3fb262c.msgpack.zlib

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docs/cassettes/agents_b6a7e925.msgpack.zlib

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docs/cassettes/agents_c96c960b.msgpack.zlib

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docs/cassettes/chatbot_16.msgpack.zlib

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docs/cassettes/chatbot_17.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/chatbot_19.msgpack.zlib

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docs/cassettes/chatbot_21.msgpack.zlib

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docs/cassettes/chatbot_28.msgpack.zlib

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docs/cassettes/chatbot_29.msgpack.zlib

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docs/cassettes/chatbot_34.msgpack.zlib

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docs/cassettes/chatbot_36.msgpack.zlib

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docs/cassettes/chatbot_43.msgpack.zlib

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docs/cassettes/chatbot_45.msgpack.zlib

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docs/cassettes/chatbot_48.msgpack.zlib

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docs/cassettes/chatbot_5.msgpack.zlib

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docs/cassettes/chatbot_7.msgpack.zlib

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docs/cassettes/chatbot_9.msgpack.zlib

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docs/cassettes/classification_0bdfcb05.msgpack.zlib

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docs/cassettes/classification_1c12fa00.msgpack.zlib

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docs/cassettes/classification_5509b6a6.msgpack.zlib

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docs/cassettes/classification_9154474c.msgpack.zlib

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docs/cassettes/data_generation_8a5528d2.msgpack.zlib

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docs/cassettes/data_generation_94e98bc4.msgpack.zlib

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docs/cassettes/data_generation_b116c487.msgpack.zlib

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docs/cassettes/data_generation_ff823394.msgpack.zlib

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docs/cassettes/extraction_13165ac8-a1dc-44ce-a6ed-f52b577473e4.msgpack.zlib

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docs/cassettes/extraction_cf7062cc-1d1d-4a37-9122-509d1b87f0a6.msgpack.zlib

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docs/cassettes/llm_chain_1b2481f0.msgpack.zlib

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docs/cassettes/llm_chain_3e45595a.msgpack.zlib

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docs/cassettes/llm_chain_3e82f933.msgpack.zlib

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docs/cassettes/llm_chain_6bacb837.msgpack.zlib

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docs/cassettes/qa_chat_history_d6d70833-b958-4cd7-9e27-29c1c08bb1b8.msgpack.zlib

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docs/cassettes/rag_ff01d415-7b0f-469d-bfda-b9cb672da611.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/docs/additional_resources/arxiv_references.mdx

@@ -13,45 +13,45 @@ From the opposite direction, scientists use `LangChain` in research and referenc
 
 | arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
 |------------------|---------|-------------------|------------------------|
-| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 | `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren,  et al. | 2024‑02‑06 | `Cookbook:` [Self-Discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
-| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 | `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al. | 2024‑01‑31 | `Cookbook:` [Raptor](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
-| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
-| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
+| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 | `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al. | 2024‑01‑08 | `Cookbook:` [Together Ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
 | `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023‑12‑11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
 | `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023‑11‑15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
-| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
+| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
+| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
 | `2307.15337v3` [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](http://arxiv.org/abs/2307.15337v3) | Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al. | 2023‑07‑28 | `Template:` [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
 | `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone,  et al. | 2023‑07‑18 | `Cookbook:` [Semi Structured Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
-| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
+| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
 | `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023‑05‑23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [Rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
 | `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023‑05‑15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [Tree Of Thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
 | `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023‑05‑06 | `Cookbook:` [Plan And Execute Agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
-| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 | `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023‑04‑17 | `Cookbook:` [Semi Structured Multi Modal Rag Llama2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi Structured And Multi Modal Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
 | `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023‑04‑07 | `Cookbook:` [Generative Agents Interactive Simulacra Of Human Behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [Multiagent Bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
 | `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023‑03‑31 | `Cookbook:` [Camel Role Playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
 | `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023‑03‑30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [Hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
 | `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023‑01‑24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
-| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
+| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
+| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
 | `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022‑12‑12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
 | `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022‑11‑25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
 | `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022‑11‑18 | `API:` [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [Program Aided Language Model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
-| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
+| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
+| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
+| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
 | `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
+| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 | `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022‑02‑01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
+| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
 | `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
-| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 | `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019‑09‑11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 ## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
@@ -60,7 +60,7 @@ From the opposite direction, scientists use `LangChain` in research and referenc
 - **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2)  **Published Date:** 2024-03-21
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** Retrieval-Augmented Large Language Models (LLMs), which incorporate the
 non-parametric knowledge from external knowledge bases into LLMs, have emerged
@@ -113,7 +113,7 @@ commonalities with human reasoning patterns.
 - **arXiv id:** [2402.03367v2](http://arxiv.org/abs/2402.03367v2)  **Published Date:** 2024-01-31
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** Infineon has identified a need for engineers, account managers, and customers
 to rapidly obtain product information. This problem is traditionally addressed
@@ -159,7 +159,7 @@ benchmark by 20% in absolute accuracy.
 - **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
 
 **Abstract:** Large language models (LLMs) inevitably exhibit hallucinations since the
@@ -187,7 +187,7 @@ performance of RAG-based approaches.
 - **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1)  **Published Date:** 2024-01-16
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** Code generation problems differ from common natural language problems - they
 require matching the exact syntax of the target language, identifying happy
@@ -293,7 +293,7 @@ outside the pre-training knowledge scope.
 - **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
 
 **Abstract:** Despite their remarkable capabilities, large language models (LLMs) often
@@ -324,7 +324,7 @@ to these models.
 - **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
    - **Cookbook:** [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
 
@@ -384,7 +384,7 @@ contribute to the responsible development of LLMs.
 - **arXiv id:** [2307.03172v3](http://arxiv.org/abs/2307.03172v3)  **Published Date:** 2023-07-06
 - **LangChain:**
 
-   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
+   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
 
 **Abstract:** While recent language models have the ability to take long contexts as input,
 relatively little is known about how well they use longer context. We analyze
@@ -451,7 +451,8 @@ steps of the thought-process and explore other directions from there. To verify
 the effectiveness of the proposed technique, we implemented a ToT-based solver
 for the Sudoku Puzzle. Experimental results show that the ToT framework can
 significantly increase the success rate of Sudoku puzzle solving. Our
-implementation of the ToT-based Sudoku solver is available on [GitHub](https://github.com/jieyilong/tree-of-thought-puzzle-solver).
+implementation of the ToT-based Sudoku solver is available on GitHub:
+\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
                 
 ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 
@@ -489,7 +490,7 @@ https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
 - **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
 - **LangChain:**
 
-   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression)
+   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression)
    - **API Reference:** [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 
 **Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
@@ -648,7 +649,7 @@ family, and discuss robustness and security.
 - **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
    - **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
    - **Cookbook:** [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
@@ -677,7 +678,7 @@ search, QA, fact verification) and languages~(e.g. sw, ko, ja).
 - **arXiv id:** [2212.08073v1](http://arxiv.org/abs/2212.08073v1)  **Published Date:** 2022-12-15
 - **LangChain:**
 
-   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
+   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
 
 **Abstract:** As AI systems become more capable, we would like to enlist their help to
 supervise other AIs. We experiment with methods for training a harmless AI
@@ -791,7 +792,7 @@ publicly available at http://reasonwithpal.com/ .
 - **arXiv id:** [2210.11934v2](http://arxiv.org/abs/2210.11934v2)  **Published Date:** 2022-10-21
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** We study hybrid search in text retrieval where lexical and semantic search
 are fused together with the intuition that the two are complementary in how
@@ -810,7 +811,7 @@ training examples to tune its only parameter to a target domain.
 - **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
 
 **Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
@@ -842,7 +843,7 @@ Project site with code: https://react-lm.github.io
 - **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
+   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
 
 **Abstract:** Traditional data lakes provide critical data infrastructure for analytical
 workloads by enabling time travel, running SQL queries, ingesting data with
@@ -867,7 +868,7 @@ TensorFlow, JAX, and integrate with numerous MLOps tools.
 - **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
+   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
 
 **Abstract:** Learned representations are a central component in modern ML systems, serving
 a multitude of downstream tasks. When training such representations, it is
@@ -924,7 +925,7 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
 - **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
 - **LangChain:**
 
-   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa)
+   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa)
    - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 
 **Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
@@ -970,7 +971,7 @@ reducing degenerate repetitions.
 - **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3)  **Published Date:** 2021-12-02
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
+   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
 
 **Abstract:** Neural information retrieval (IR) has greatly advanced search and other
 knowledge-intensive language tasks. While many neural IR methods encode queries
@@ -1021,7 +1022,7 @@ https://github.com/OpenAI/CLIP.
 - **arXiv id:** [2005.14165v4](http://arxiv.org/abs/2005.14165v4)  **Published Date:** 2020-05-28
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** Recent work has demonstrated substantial gains on many NLP tasks and
 benchmarks by pre-training on a large corpus of text followed by fine-tuning on
@@ -1054,7 +1055,7 @@ and of GPT-3 in general.
 - **arXiv id:** [2005.11401v4](http://arxiv.org/abs/2005.11401v4)  **Published Date:** 2020-05-22
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
 
 **Abstract:** Large pre-trained language models have been shown to store factual knowledge
 in their parameters, and achieve state-of-the-art results when fine-tuned on

docs/docs/concepts.mdx

@@ -97,7 +97,7 @@ For guides on how to do specific tasks with LCEL, check out [the relevant how-to
 ### Runnable interface
 <span data-heading-keywords="invoke,runnable"></span>
 
-To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
+To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
 
 This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way.
 The standard interface includes:
@@ -116,14 +116,14 @@ These also have corresponding async methods that should be used with [asyncio](h
 
 The **input type** and **output type** varies by component:
 
-| Component    | Input Type                                            | Output Type           |
-|--------------|-------------------------------------------------------|-----------------------|
-| Prompt       | Dictionary                                            | PromptValue           |
-| ChatModel    | Single string, list of chat messages or a PromptValue | ChatMessage           |
-| LLM          | Single string, list of chat messages or a PromptValue | String                |
-| OutputParser | The output of an LLM or ChatModel                     | Depends on the parser |
-| Retriever    | Single string                                         | List of Documents     |
-| Tool         | Single string or dictionary, depending on the tool    | Depends on the tool   |
+| Component | Input Type | Output Type |
+| --- | --- | --- |
+| Prompt | Dictionary | PromptValue |
+| ChatModel | Single string, list of chat messages or a PromptValue | ChatMessage |
+| LLM | Single string, list of chat messages or a PromptValue | String |
+| OutputParser | The output of an LLM or ChatModel | Depends on the parser |
+| Retriever | Single string | List of Documents |
+| Tool | Single string or dictionary, depending on the tool | Depends on the tool |
 
 
 All runnables expose input and output **schemas** to inspect the inputs and outputs:
@@ -236,7 +236,7 @@ This is where information like log-probs and token usage may be stored.
 
 **`tool_calls`**
 
-These represent a decision from a language model to call a tool. They are included as part of an `AIMessage` output.
+These represent a decision from an language model to call a tool. They are included as part of an `AIMessage` output.
 They can be accessed from there with the `.tool_calls` property.
 
 This property returns a list of `ToolCall`s. A `ToolCall` is a dictionary with the following arguments:
@@ -256,8 +256,6 @@ This represents a message with role "tool", which contains the result of calling
 - a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
 - an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
 
-With most chat models, a `ToolMessage` can only appear in the chat history after an `AIMessage` that has a populated `tool_calls` field.
-
 #### (Legacy) FunctionMessage
 
 This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
@@ -382,17 +380,17 @@ LangChain has lots of different types of output parsers. This is a list of outpu
 
 | Name            | Supports Streaming | Has Format Instructions       | Calls LLM | Input Type                       | Output Type          | Description                                                                                                                                                                                                                                              |
 |-----------------|--------------------|-------------------------------|-----------|----------------------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html#langchain_core.output_parsers.json.JsonOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`               | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
-| [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`                 | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
-| [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)           | ✅                  | ✅                             |           | `str` \| `Message`                 | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
-| [OutputFixing](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)    |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
-| [RetryWithError](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)  |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
-| [Pydantic](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
-| [YAML](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                                                    |
-| [PandasDataFrame](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                             |           | `str` \| `Message`                 | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
-| [Enum](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)            |                    | ✅                             |           | `str` \| `Message`                 | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
-| [Datetime](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
-| [Structured](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)      |                    | ✅                             |           | `str` \| `Message`                 | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
+| [JSON](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html#langchain_core.output_parsers.json.JsonOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`               | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
+| [XML](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`                 | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
+| [CSV](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)           | ✅                  | ✅                             |           | `str` \| `Message`                 | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
+| [OutputFixing](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)    |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
+| [RetryWithError](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)  |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
+| [Pydantic](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
+| [YAML](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                                                    |
+| [PandasDataFrame](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                             |           | `str` \| `Message`                 | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
+| [Enum](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)            |                    | ✅                             |           | `str` \| `Message`                 | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
+| [Datetime](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
+| [Structured](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)      |                    | ✅                             |           | `str` \| `Message`                 | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
 
 For specifics on how to use output parsers, see the [relevant how-to guides here](/docs/how_to/#output-parsers).
 
@@ -503,7 +501,7 @@ For specifics on how to use retrievers, see the [relevant how-to guides here](/d
 For some techniques, such as [indexing and retrieval with multiple vectors per document](/docs/how_to/multi_vector/) or
 [caching embeddings](/docs/how_to/caching_embeddings/), having a form of key-value (KV) storage is helpful.
 
-LangChain includes a [`BaseStore`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
+LangChain includes a [`BaseStore`](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
 which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
 more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
 encoding and decoding data for their specific needs.
@@ -512,7 +510,7 @@ This means that as a user, you only need to think about one type of store rather
 
 #### Interface
 
-All [`BaseStores`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
+All [`BaseStores`](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
 for modifying **multiple** key-value pairs at once:
 
 - `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
@@ -597,10 +595,10 @@ tool_call = ai_msg.tool_calls[0]
 # -> ToolCall(args={...}, id=..., ...)
 tool_message = tool.invoke(tool_call)
 # -> ToolMessage(
-#      content="tool result foobar...",
-#      tool_call_id=...,
-#      name="tool_name"
-#    )
+    content="tool result foobar...",
+    tool_call_id=...,
+    name="tool_name"
+)
 ```
 
 If you are invoking the tool this way and want to include an [artifact](/docs/concepts/#toolmessage) for the ToolMessage, you will need to have the tool return two things.
@@ -710,15 +708,17 @@ You can subscribe to these events by using the `callbacks` argument available th
 
 Callback handlers can either be `sync` or `async`:
 
-* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
-* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
+* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
+* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
 
-During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
+During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
 
 #### Passing callbacks
 
 The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
 
+The callbacks are available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
+
 - **Request time callbacks**: Passed at the time of the request in addition to the input data.
     Available on all standard `Runnable` objects. These callbacks are INHERITED by all children
     of the object they are defined on. For example, `chain.invoke({"number": 25}, {"callbacks": [handler]})`.
@@ -734,10 +734,10 @@ of the object.
 If you're creating a custom chain or runnable, you need to remember to propagate request time
 callbacks to any child objects.
 
-:::important Async in Python&lt;=3.10
+:::important Async in Python<=3.10
 
 Any `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables
-and is running `async` in python&lt;=3.10, will have to propagate callbacks to child
+and is running `async` in python<=3.10, will have to propagate callbacks to child
 objects manually. This is because LangChain cannot automatically propagate
 callbacks to child objects in this case.
 
@@ -779,7 +779,7 @@ For models (or other components) that don't support streaming natively, this ite
 you could still use the same general pattern when calling them. Using `.stream()` will also automatically call the model in streaming mode
 without the need to provide additional config.
 
-The type of each outputted chunk depends on the type of component - for example, chat models yield [`AIMessageChunks`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html).
+The type of each outputted chunk depends on the type of component - for example, chat models yield [`AIMessageChunks`](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html).
 Because this method is part of [LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel),
 you can handle formatting differences from different outputs using an [output parser](/docs/concepts/#output-parsers) to transform
 each yielded chunk.
@@ -827,10 +827,10 @@ including a table listing available events.
 #### Callbacks
 
 The lowest level way to stream outputs from LLMs in LangChain is via the [callbacks](/docs/concepts/#callbacks) system. You can pass a
-callback handler that handles the [`on_llm_new_token`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_new_token) event into LangChain components. When that component is invoked, any
+callback handler that handles the [`on_llm_new_token`](https://python.langchain.com/v0.2/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_new_token) event into LangChain components. When that component is invoked, any
 [LLM](/docs/concepts/#llms) or [chat model](/docs/concepts/#chat-models) contained in the component calls
 the callback with the generated token. Within the callback, you could pipe the tokens into some other destination, e.g. a HTTP response.
-You can also handle the [`on_llm_end`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_end) event to perform any necessary cleanup.
+You can also handle the [`on_llm_end`](https://python.langchain.com/v0.2/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_end) event to perform any necessary cleanup.
 
 You can see [this how-to section](/docs/how_to/#callbacks) for more specifics on using callbacks.
 
@@ -945,7 +945,7 @@ Here's an example:
 ```python
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 
 
 class Joke(BaseModel):
@@ -1062,7 +1062,7 @@ a `tool_calls` field containing `args` that match the desired shape.
 There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
 
 ```python
-from pydantic import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_openai import ChatOpenAI
 
 class ResponseFormatter(BaseModel):

docs/docs/contributing/code/setup.mdx

@@ -73,9 +73,9 @@ make docker_tests
 
 There are also [integration tests and code-coverage](/docs/contributing/testing/) available.
 
-### Only develop langchain_core or langchain_community
+### Only develop langchain_core or langchain_experimental
 
-If you are only developing `langchain_core` or `langchain_community`, you can simply install the dependencies for the respective projects and run tests:
+If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
 
 ```bash
 cd libs/core
@@ -86,7 +86,7 @@ make test
 Or:
 
 ```bash
-cd libs/community
+cd libs/experimental
 poetry install --with test
 make test
 ```