x

.devcontainer/docker-compose.yaml

@@ -5,10 +5,10 @@ services:
       dockerfile: libs/langchain/dev.Dockerfile
       context: ..
     volumes:
-      # Update this to wherever you want VS Code to mount the folder of your project
+   # Update this to wherever you want VS Code to mount the folder of your project
       - ..:/workspaces/langchain:cached
     networks:
-      - langchain-network
+      - langchain-network 
   #   environment:
   #     MONGO_ROOT_USERNAME: root
   #     MONGO_ROOT_PASSWORD: example123
@@ -28,3 +28,5 @@ services:
 networks:
   langchain-network:
     driver: bridge
+    
+    

.github/ISSUE_TEMPLATE/config.yml

@@ -4,6 +4,9 @@ contact_links:
   - name: 🤔 Question or Problem
     about: Ask a question or ask about a problem in GitHub Discussions.
     url: https://www.github.com/langchain-ai/langchain/discussions/categories/q-a
+  - name: Discord
+    url: https://discord.gg/6adMQxSpJS
+    about: General community discussions
   - name: Feature Request
     url: https://www.github.com/langchain-ai/langchain/discussions/categories/ideas
     about: Suggest a feature or an idea

.github/actions/people/app/main.py

@@ -350,7 +350,11 @@ def get_graphql_pr_edges(*, settings: Settings, after: Union[str, None] = None):
         print("Querying PRs...")
     else:
         print(f"Querying PRs with cursor {after}...")
-    data = get_graphql_response(settings=settings, query=prs_query, after=after)
+    data = get_graphql_response(
+        settings=settings,
+        query=prs_query,
+        after=after
+    )
     graphql_response = PRsResponse.model_validate(data)
     return graphql_response.data.repository.pullRequests.edges
 
@@ -480,16 +484,10 @@ def get_contributors(settings: Settings):
             lines_changed = pr.additions + pr.deletions
             score = _logistic(files_changed, 20) + _logistic(lines_changed, 100)
             contributor_scores[pr.author.login] += score
-            three_months_ago = datetime.now(timezone.utc) - timedelta(days=3 * 30)
+            three_months_ago = (datetime.now(timezone.utc) - timedelta(days=3*30))
             if pr.createdAt > three_months_ago:
                 recent_contributor_scores[pr.author.login] += score
-    return (
-        contributors,
-        contributor_scores,
-        recent_contributor_scores,
-        reviewers,
-        authors,
-    )
+    return contributors, contributor_scores, recent_contributor_scores, reviewers, authors
 
 
 def get_top_users(
@@ -526,13 +524,9 @@ if __name__ == "__main__":
     # question_commentors, question_last_month_commentors, question_authors = get_experts(
     #     settings=settings
     # )
-    (
-        contributors,
-        contributor_scores,
-        recent_contributor_scores,
-        reviewers,
-        pr_authors,
-    ) = get_contributors(settings=settings)
+    contributors, contributor_scores, recent_contributor_scores, reviewers, pr_authors = get_contributors(
+        settings=settings
+    )
     # authors = {**question_authors, **pr_authors}
     authors = {**pr_authors}
     maintainers_logins = {
@@ -553,7 +547,6 @@ if __name__ == "__main__":
         "obi1kenobi",
         "langchain-infra",
         "jacoblee93",
-        "isahers1",
         "dqbd",
         "bracesproul",
         "akira",
@@ -565,7 +558,7 @@ if __name__ == "__main__":
         maintainers.append(
             {
                 "login": login,
-                "count": contributors[login],  # + question_commentors[login],
+                "count": contributors[login], #+ question_commentors[login],
                 "avatarUrl": user.avatarUrl,
                 "twitterUsername": user.twitterUsername,
                 "url": user.url,
@@ -621,7 +614,9 @@ if __name__ == "__main__":
     new_people_content = yaml.dump(
         people, sort_keys=False, width=200, allow_unicode=True
     )
-    if people_old_content == new_people_content:
+    if (
+        people_old_content == new_people_content
+    ):
         logging.info("The LangChain People data hasn't changed, finishing.")
         sys.exit(0)
     people_path.write_text(new_people_content, encoding="utf-8")
@@ -634,7 +629,9 @@ if __name__ == "__main__":
     logging.info(f"Creating a new branch {branch_name}")
     subprocess.run(["git", "checkout", "-B", branch_name], check=True)
     logging.info("Adding updated file")
-    subprocess.run(["git", "add", str(people_path)], check=True)
+    subprocess.run(
+        ["git", "add", str(people_path)], check=True
+    )
     logging.info("Committing updated file")
     message = "👥 Update LangChain people data"
     result = subprocess.run(["git", "commit", "-m", message], check=True)
@@ -643,4 +640,4 @@ if __name__ == "__main__":
     logging.info("Creating PR")
     pr = repo.create_pull(title=message, body=message, base="master", head=branch_name)
     logging.info(f"Created PR: {pr.number}")
-    logging.info("Finished")
+    logging.info("Finished")

.github/scripts/check_diff.py

@@ -1,12 +1,7 @@
-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 os
+from typing import Dict
 
 LANGCHAIN_DIRS = [
     "libs/core",
@@ -16,144 +11,6 @@ LANGCHAIN_DIRS = [
     "libs/experimental",
 ]
 
-# 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
-IGNORED_PARTNERS = [
-    # remove huggingface from dependents because of CI instability
-    # specifically in huggingface jobs
-    # https://github.com/langchain-ai/langchain/issues/25558
-    "huggingface",
-]
-
-
-def all_package_dirs() -> Set[str]:
-    return {
-        "/".join(path.split("/")[:-1]).lstrip("./")
-        for path in glob.glob("./libs/**/pyproject.toml", recursive=True)
-        if "libs/cli" not in path and "libs/standard-tests" not in path
-    }
-
-
-def dependents_graph() -> dict:
-    """
-    Construct a mapping of package -> dependents, such that we can
-    run tests on all dependents of a package when a change is made.
-    """
-    dependents = defaultdict(set)
-
-    for path in glob.glob("./libs/**/pyproject.toml", recursive=True):
-        if "template" in path:
-            continue
-
-        # load regular and test deps from pyproject.toml
-        with open(path, "rb") as f:
-            pyproject = tomllib.load(f)["tool"]["poetry"]
-
-        pkg_dir = "libs" + "/".join(path.split("libs")[1].split("/")[:-1])
-        for dep in [
-            *pyproject["dependencies"].keys(),
-            *pyproject["group"]["test"]["dependencies"].keys(),
-        ]:
-            if "langchain" in dep:
-                dependents[dep].add(pkg_dir)
-                continue
-
-        # load extended deps from extended_testing_deps.txt
-        package_path = Path(path).parent
-        extended_requirement_path = package_path / "extended_testing_deps.txt"
-        if extended_requirement_path.exists():
-            with open(extended_requirement_path, "r") as f:
-                extended_deps = f.read().splitlines()
-                for depline in extended_deps:
-                    if depline.startswith("-e "):
-                        # editable dependency
-                        assert depline.startswith(
-                            "-e ../partners/"
-                        ), "Extended test deps should only editable install partner packages"
-                        partner = depline.split("partners/")[1]
-                        dep = f"langchain-{partner}"
-                    else:
-                        dep = depline.split("==")[0]
-
-                    if "langchain" in dep:
-                        dependents[dep].add(pkg_dir)
-
-    for k in dependents:
-        for partner in IGNORED_PARTNERS:
-            if f"libs/partners/{partner}" in dependents[k]:
-                dependents[k].remove(f"libs/partners/{partner}")
-    return dependents
-
-
-def add_dependents(dirs_to_eval: Set[str], dependents: dict) -> List[str]:
-    updated = set()
-    for dir_ in dirs_to_eval:
-        # handle core manually because it has so many dependents
-        if "core" in dir_:
-            updated.add(dir_)
-            continue
-        pkg = "langchain-" + dir_.split("/")[-1]
-        updated.update(dependents[pkg])
-        updated.add(dir_)
-    return list(updated)
-
-
-def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
-    if dir_ == "libs/core":
-        return [
-            {"working-directory": dir_, "python-version": f"3.{v}"}
-            for v in range(8, 13)
-        ]
-    min_python = "3.9"
-    max_python = "3.12"
-
-    # custom logic for specific directories
-    if dir_ == "libs/partners/milvus":
-        # milvus poetry doesn't allow 3.12 because they
-        # declare deps in funny way
-        max_python = "3.11"
-
-    if dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
-        # community extended test resolution in 3.12 is slow
-        # even in uv
-        max_python = "3.11"
-
-    if dir_ == "libs/community" and job == "compile-integration-tests":
-        # community integration deps are slow in 3.12
-        max_python = "3.11"
-
-    return [
-        {"working-directory": dir_, "python-version": min_python},
-        {"working-directory": dir_, "python-version": max_python},
-    ]
-
-
-def _get_configs_for_multi_dirs(
-    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"]:
-        dirs = add_dependents(
-            dirs_to_run["test"] | dirs_to_run["extended-test"], dependents
-        )
-    elif job == "extended-tests":
-        dirs = list(dirs_to_run["extended-test"])
-    else:
-        raise ValueError(f"Unknown job: {job}")
-
-    return [
-        config for dir_ in dirs for config in _get_configs_for_single_dir(job, dir_)
-    ]
-
-
 if __name__ == "__main__":
     files = sys.argv[1:]
 
@@ -164,11 +21,10 @@ if __name__ == "__main__":
     }
     docs_edited = False
 
-    if len(files) >= 300:
+    if len(files) == 300:
         # max diff length is 300 files - there are likely files missing
-        dirs_to_run["lint"] = all_package_dirs()
-        dirs_to_run["test"] = all_package_dirs()
-        dirs_to_run["extended-test"] = set(LANGCHAIN_DIRS)
+        raise ValueError("Max diff reached. Please manually run CI on changed libs.")
+
     for file in files:
         if any(
             file.startswith(dir_)
@@ -188,9 +44,6 @@ if __name__ == "__main__":
             # for extended testing
             found = False
             for dir_ in LANGCHAIN_DIRS:
-                if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS:
-                    dirs_to_run["extended-test"].add(dir_)
-                    continue
                 if file.startswith(dir_):
                     found = True
                 if found:
@@ -202,6 +55,7 @@ if __name__ == "__main__":
             dirs_to_run["test"].add("libs/partners/mistralai")
             dirs_to_run["test"].add("libs/partners/openai")
             dirs_to_run["test"].add("libs/partners/anthropic")
+            dirs_to_run["test"].add("libs/partners/ai21")
             dirs_to_run["test"].add("libs/partners/fireworks")
             dirs_to_run["test"].add("libs/partners/groq")
 
@@ -227,25 +81,14 @@ if __name__ == "__main__":
                 docs_edited = True
             dirs_to_run["lint"].add(".")
 
-    dependents = dependents_graph()
-
-    # 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 [
-            "lint",
-            "test",
-            "extended-tests",
-            "compile-integration-tests",
-            "dependencies",
-        ]
+    outputs = {
+        "dirs-to-lint": list(
+            dirs_to_run["lint"] | dirs_to_run["test"] | dirs_to_run["extended-test"]
+        ),
+        "dirs-to-test": list(dirs_to_run["test"] | dirs_to_run["extended-test"]),
+        "dirs-to-extended-test": list(dirs_to_run["extended-test"]),
+        "docs-edited": "true" if docs_edited else "",
     }
-    map_job_to_configs["test-doc-imports"] = (
-        [{"python-version": "3.12"}] if docs_edited else []
-    )
-
-    for key, value in map_job_to_configs.items():
+    for key, value in outputs.items():
         json_output = json.dumps(value)
         print(f"{key}={json_output}")

.github/scripts/check_prerelease_dependencies.py

@@ -1,35 +0,0 @@
-import sys
-import tomllib
-
-if __name__ == "__main__":
-    # Get the TOML file path from the command line argument
-    toml_file = sys.argv[1]
-
-    # read toml file
-    with open(toml_file, "rb") as file:
-        toml_data = tomllib.load(file)
-
-    # see if we're releasing an rc
-    version = toml_data["tool"]["poetry"]["version"]
-    releasing_rc = "rc" in version or "dev" in version
-
-    # if not, iterate through dependencies and make sure none allow prereleases
-    if not releasing_rc:
-        dependencies = toml_data["tool"]["poetry"]["dependencies"]
-        for lib in dependencies:
-            dep_version = dependencies[lib]
-            dep_version_string = (
-                dep_version["version"] if isinstance(dep_version, dict) else dep_version
-            )
-
-            if "rc" in dep_version_string:
-                raise ValueError(
-                    f"Dependency {lib} has a prerelease version. Please remove this."
-                )
-
-            if isinstance(dep_version, dict) and dep_version.get(
-                "allow-prereleases", False
-            ):
-                raise ValueError(
-                    f"Dependency {lib} has allow-prereleases set to true. Please remove this."
-                )

.github/scripts/get_min_versions.py

@@ -1,11 +1,6 @@
 import sys
 
-if sys.version_info >= (3, 11):
-    import tomllib
-else:
-    # for python 3.10 and below, which doesnt have stdlib tomllib
-    import tomli as tomllib
-
+import tomllib
 from packaging.version import parse as parse_version
 import re
 
@@ -14,11 +9,8 @@ MIN_VERSION_LIBS = [
     "langchain-community",
     "langchain",
     "langchain-text-splitters",
-    "SQLAlchemy",
 ]
 
-SKIP_IF_PULL_REQUEST = ["langchain-core"]
-
 
 def get_min_version(version: str) -> str:
     # base regex for x.x.x with cases for rc/post/etc
@@ -45,7 +37,7 @@ 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):
+def get_min_version_from_toml(toml_path: str):
     # Parse the TOML file
     with open(toml_path, "rb") as file:
         toml_data = tomllib.load(file)
@@ -58,10 +50,6 @@ def get_min_version_from_toml(toml_path: str, versions_for: str):
 
     # Iterate over the libs in MIN_VERSION_LIBS
     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
-            continue
         # Check if the lib is present in the dependencies
         if lib in dependencies:
             # Get the version string
@@ -82,10 +70,10 @@ def get_min_version_from_toml(toml_path: str, versions_for: str):
 if __name__ == "__main__":
     # Get the TOML file path from the command line argument
     toml_file = sys.argv[1]
-    versions_for = sys.argv[2]
-    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)
+    min_versions = get_min_version_from_toml(toml_file)
 
-    print(" ".join([f"{lib}=={version}" for lib, version in min_versions.items()]))
+    print(
+        " ".join([f"{lib}=={version}" for lib, version in min_versions.items()])
+    )

.github/workflows/_compile_integration_test.yml

@@ -7,10 +7,6 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
 
 env:
   POETRY_VERSION: "1.7.1"
@@ -21,14 +17,21 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
-    name: "poetry run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: "poetry run pytest -m compile tests/integration_tests #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ inputs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: compile-integration

.github/workflows/_dependencies.yml

@@ -0,0 +1,117 @@
+name: dependencies
+
+on:
+  workflow_call:
+    inputs:
+      working-directory:
+        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"
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: dependency checks ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: pydantic-cross-compat
+
+      - name: Install dependencies
+        shell: bash
+        run: poetry install
+
+      - name: Check imports with base dependencies
+        shell: bash
+        run: poetry run make check_imports
+
+      - name: Install test 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: Install the opposite major version of pydantic
+        # If normal tests use pydantic v1, here we'll use v2, and vice versa.
+        shell: bash
+        # airbyte currently doesn't support pydantic v2
+        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
+        run: |
+          # Determine the major part of pydantic version
+          REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
+
+          if [[ "$REGULAR_VERSION" == "1" ]]; then
+            PYDANTIC_DEP=">=2.1,<3"
+            TEST_WITH_VERSION="2"
+          elif [[ "$REGULAR_VERSION" == "2" ]]; then
+            PYDANTIC_DEP="<2"
+            TEST_WITH_VERSION="1"
+          else
+            echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test."
+            exit 1
+          fi
+
+          # Install via `pip` instead of `poetry add` to avoid changing lockfile,
+          # which would prevent caching from working: the cache would get saved
+          # to a different key than where it gets loaded from.
+          poetry run pip install "pydantic${PYDANTIC_DEP}"
+
+          # Ensure that the correct pydantic is installed now.
+          echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}"
+
+          # Determine the major part of pydantic version
+          CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
+
+          # Check that the major part of pydantic version is as expected, if not
+          # raise an error
+          if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then
+            echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}"
+            exit 1
+          fi
+          echo "Found pydantic version ${CURRENT_VERSION}, as expected"
+      - name: Run pydantic compatibility tests
+        # airbyte currently doesn't support pydantic v2
+        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
+        shell: bash
+        run: make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(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/_integration_test.yml

@@ -6,28 +6,30 @@ on:
       working-directory:
         required: true
         type: string
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
 
 env:
   POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
+    environment: Scheduled testing
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
-    name: Python ${{ inputs.python-version }}
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.11"
+    name: Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ inputs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: core
@@ -51,15 +53,8 @@ jobs:
         shell: bash
         env:
           AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

.github/workflows/_lint.yml

@@ -11,10 +11,6 @@ on:
         required: false
         type: string
         description: "Relative path to the langchain library folder"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
 
 env:
   POETRY_VERSION: "1.7.1"
@@ -25,15 +21,27 @@ env:
 
 jobs:
   build:
-    name: "make lint #${{ inputs.python-version }}"
+    name: "make lint #${{ matrix.python-version }}"
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        # Only lint on the min and max supported Python versions.
+        # It's extremely unlikely that there's a lint issue on any version in between
+        # that doesn't show up on the min or max versions.
+        #
+        # GitHub rate-limits how many jobs can be running at any one time.
+        # Starting new jobs is also relatively slow,
+        # so linting on fewer versions makes CI faster.
+        python-version:
+          - "3.8"
+          - "3.11"
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ inputs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: lint-with-extras
@@ -78,7 +86,7 @@ jobs:
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ inputs.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
 
       - name: Analysing the code with our lint
@@ -112,7 +120,7 @@ jobs:
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ inputs.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_release.yml

@@ -122,6 +122,7 @@ jobs:
           fi
           {
             echo 'release-body<<EOF'
+            echo "# Release $TAG"
             echo $PREAMBLE
             echo
             git log --format="%s" "$PREV_TAG"..HEAD -- $WORKING_DIR
@@ -134,7 +135,6 @@ jobs:
       - release-notes
     uses:
       ./.github/workflows/_test_release.yml
-    permissions: write-all
     with:
       working-directory: ${{ inputs.working-directory }}
       dangerous-nonmaster-release: ${{ inputs.dangerous-nonmaster-release }}
@@ -189,7 +189,7 @@ jobs:
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION" || \
           ( \
-            sleep 15 && \
+            sleep 5 && \
             poetry run pip install \
               --extra-index-url https://test.pypi.org/simple/ \
               "$PKG_NAME==$VERSION" \
@@ -202,7 +202,7 @@ jobs:
           poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with test
+        run: poetry install --with test,test_integration
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the test PyPI version.
@@ -221,17 +221,12 @@ jobs:
         run: make tests
         working-directory: ${{ inputs.working-directory }}
 
-      - name: Check for prerelease versions
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
-
       - name: Get minimum versions
         working-directory: ${{ inputs.working-directory }}
         id: min-version
         run: |
           poetry run pip install packaging
-          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml release)"
+          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)"
           echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
           echo "min-versions=$min_versions"
 
@@ -250,10 +245,6 @@ jobs:
         with:
           credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
 
-      - name: Import integration test dependencies
-        run: poetry install --with test,test_integration
-        working-directory: ${{ inputs.working-directory }}
-
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
@@ -290,7 +281,6 @@ 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

@@ -11,10 +11,6 @@ on:
         required: false
         type: string
         description: "Relative path to the langchain library folder"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
 
 env:
   POETRY_VERSION: "1.7.1"
@@ -25,14 +21,21 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
-    name: "make test #${{ inputs.python-version }}"
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: "make test #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ inputs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: core
@@ -65,21 +68,3 @@ jobs:
           # grep will exit non-zero if the target message isn't found,
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
-          
-      - 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

@@ -2,11 +2,6 @@ name: test_doc_imports
 
 on:
   workflow_call:
-    inputs:
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
 
 env:
   POETRY_VERSION: "1.7.1"
@@ -14,14 +9,18 @@ env:
 jobs:
   build:
     runs-on: ubuntu-latest
-    name: "check doc imports #${{ inputs.python-version }}"
+    strategy:
+      matrix:
+        python-version:
+          - "3.11"
+    name: "check doc imports #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ inputs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           cache-key: core
 

.github/workflows/check_diffs.yml

@@ -26,90 +26,97 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
+          python-version: '3.10'
       - id: files
         uses: Ana06/get-changed-files@v2.2.0
       - id: set-matrix
         run: |
           python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
-      lint: ${{ steps.set-matrix.outputs.lint }}
-      test: ${{ steps.set-matrix.outputs.test }}
-      extended-tests: ${{ steps.set-matrix.outputs.extended-tests }}
-      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 }}
+      dirs-to-lint: ${{ steps.set-matrix.outputs.dirs-to-lint }}
+      dirs-to-test: ${{ steps.set-matrix.outputs.dirs-to-test }}
+      dirs-to-extended-test: ${{ steps.set-matrix.outputs.dirs-to-extended-test }}
+      docs-edited: ${{ steps.set-matrix.outputs.docs-edited }}
   lint:
-    name: cd ${{ matrix.job-configs.working-directory }}
+    name: cd ${{ matrix.working-directory }}
     needs: [ build ]
-    if: ${{ needs.build.outputs.lint != '[]' }}
+    if: ${{ needs.build.outputs.dirs-to-lint != '[]' }}
     strategy:
       matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.lint) }}
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-lint) }}
     uses: ./.github/workflows/_lint.yml
     with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
+      working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   test:
-    name: cd ${{ matrix.job-configs.working-directory }}
+    name: cd ${{ matrix.working-directory }}
     needs: [ build ]
-    if: ${{ needs.build.outputs.test != '[]' }}
+    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
     strategy:
       matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.test) }}
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
     uses: ./.github/workflows/_test.yml
     with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
+      working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   test-doc-imports:
     needs: [ build ]
-    if: ${{ needs.build.outputs.test-doc-imports != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.test-doc-imports) }}
+    if: ${{ needs.build.outputs.dirs-to-test != '[]' || needs.build.outputs.docs-edited }}
     uses: ./.github/workflows/_test_doc_imports.yml
     secrets: inherit
-    with:
-      python-version: ${{ matrix.job-configs.python-version }}
 
   compile-integration-tests:
-    name: cd ${{ matrix.job-configs.working-directory }}
+    name: cd ${{ matrix.working-directory }}
     needs: [ build ]
-    if: ${{ needs.build.outputs.compile-integration-tests != '[]' }}
+    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
     strategy:
       matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.compile-integration-tests) }}
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
+      working-directory: ${{ matrix.working-directory }}
+    secrets: inherit
+
+  dependencies:
+    name: cd ${{ matrix.working-directory }}
+    needs: [ build ]
+    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
+    strategy:
+      matrix:
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
+    uses: ./.github/workflows/_dependencies.yml
+    with:
+      working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   extended-tests:
-    name: "cd ${{ matrix.job-configs.working-directory }} / make extended_tests #${{ matrix.job-configs.python-version }}"
+    name: "cd ${{ matrix.working-directory }} / make extended_tests #${{ matrix.python-version }}"
     needs: [ build ]
-    if: ${{ needs.build.outputs.extended-tests != '[]' }}
+    if: ${{ needs.build.outputs.dirs-to-extended-test != '[]' }}
     strategy:
       matrix:
         # note different variable for extended test dirs
-        job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-extended-test) }}
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
     runs-on: ubuntu-latest
     defaults:
       run:
-        working-directory: ${{ matrix.job-configs.working-directory }}
+        working-directory: ${{ matrix.working-directory }}
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python ${{ matrix.job-configs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ matrix.job-configs.python-version }}
+          python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ matrix.job-configs.working-directory }}
+          working-directory: ${{ matrix.working-directory }}
           cache-key: extended
 
       - name: Install dependencies
@@ -136,7 +143,7 @@ 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]
+    needs: [build, lint, test, compile-integration-tests, dependencies, extended-tests, test-doc-imports]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/check_new_docs.yml

@@ -3,8 +3,7 @@ name: Integration docs lint
 
 on:
   push:
-    branches: [master]
-  pull_request:
+    branches: [test_docs_lint]
 
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
@@ -26,11 +25,6 @@ jobs:
           python-version: '3.10'
       - id: files
         uses: Ana06/get-changed-files@v2.2.0
-        with:
-          filter: |
-            *.ipynb
-            *.md
-            *.mdx
       - name: Check new docs
         run: |
           python docs/scripts/check_templates.py ${{ steps.files.outputs.added }}

.github/workflows/people.yml

@@ -16,7 +16,6 @@ jobs:
   langchain-people:
     if: github.repository_owner == 'langchain-ai'
     runs-on: ubuntu-latest
-    permissions: write-all
     steps:
       - name: Dump GitHub context
         env:

.github/workflows/scheduled_test.yml

@@ -17,17 +17,21 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-          - "3.9"
+          - "3.8"
           - "3.11"
         working-directory:
           - "libs/partners/openai"
           - "libs/partners/anthropic"
+          - "libs/partners/ai21"
           - "libs/partners/fireworks"
           - "libs/partners/groq"
           - "libs/partners/mistralai"
+          - "libs/partners/together"
+          - "libs/partners/cohere"
           - "libs/partners/google-vertexai"
           - "libs/partners/google-genai"
           - "libs/partners/aws"
+          - "libs/partners/nvidia-ai-endpoints"
 
     steps:
       - uses: actions/checkout@v4
@@ -37,6 +41,14 @@ jobs:
         with:
           repository: langchain-ai/langchain-google
           path: langchain-google
+      - 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-aws
@@ -46,9 +58,13 @@ jobs:
         run: |
           rm -rf \
             langchain/libs/partners/google-genai \
-            langchain/libs/partners/google-vertexai
+            langchain/libs/partners/google-vertexai \
+            langchain/libs/partners/nvidia-ai-endpoints \
+            langchain/libs/partners/cohere
           mv langchain-google/libs/genai langchain/libs/partners/google-genai
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
+          mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
+          mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
           mv langchain-aws/libs/aws langchain/libs/partners/aws
 
       - name: Set up Python ${{ matrix.python-version }}
@@ -88,9 +104,11 @@ jobs:
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
+          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
@@ -105,6 +123,8 @@ jobs:
           rm -rf \
             langchain/libs/partners/google-genai \
             langchain/libs/partners/google-vertexai \
+            langchain/libs/partners/nvidia-ai-endpoints \
+            langchain/libs/partners/cohere \
             langchain/libs/partners/aws
 
       - name: Ensure the tests did not create any additional files

.gitignore

@@ -167,14 +167,11 @@ docs/.docusaurus/
 docs/.cache-loader/
 docs/_dist
 docs/api_reference/*api_reference.rst
-docs/api_reference/*.md
 docs/api_reference/_build
 docs/api_reference/*/
 !docs/api_reference/_static/
 !docs/api_reference/templates/
 !docs/api_reference/themes/
-!docs/api_reference/_extensions/
-!docs/api_reference/scripts/
 docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock

MIGRATE.md

@@ -52,7 +52,7 @@ Now:
 
 `from langchain_experimental.sql import SQLDatabaseChain`
 
-Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out this [`SQL question-answering tutorial`](https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#convert-question-to-sql-query)
+Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out [`create_sql_query_chain`](https://github.com/langchain-ai/langchain/blob/master/docs/extras/use_cases/tabular/sql_query.ipynb)
 
 `from langchain.chains import create_sql_query_chain`
 

Makefile

@@ -31,7 +31,6 @@ docs_linkcheck:
 api_docs_build:
 	poetry run python docs/api_reference/create_api_rst.py
 	cd docs/api_reference && poetry run make html
-	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
 
 API_PKG ?= text-splitters
 
@@ -39,14 +38,12 @@ api_docs_quick_preview:
 	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
-	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
-	open docs/api_reference/_build/html/reference.html
+	open docs/api_reference/_build/html/$(shell echo $(API_PKG) | sed 's/-/_/g')_api_reference.html
 
 ## api_docs_clean: Clean the API Reference documentation build artifacts.
 api_docs_clean:
 	find ./docs/api_reference -name '*_api_reference.rst' -delete
 	git clean -fdX ./docs/api_reference
-	rm docs/api_reference/index.md
 	
 
 ## api_docs_linkcheck: Run linkchecker on the API Reference documentation.

README.md

@@ -7,27 +7,27 @@
 [![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-core?style=flat-square)](https://pypistats.org/packages/langchain-core)
 [![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain)
+[![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain?style=flat-square)](https://libraries.io/github/langchain-ai/langchain)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/issues)
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
+[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
 
 Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
-To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
-[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
+To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
+[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
 Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
 
 ## Quick Install
 
 With pip:
-
 ```bash
 pip install langchain
 ```
 
 With conda:
-
 ```bash
 conda install langchain -c conda-forge
 ```
@@ -38,28 +38,24 @@ 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/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.
+- **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) and [components](https://python.langchain.com/v0.2/docs/concepts/#components). Integrate with hundreds of [third-party providers](https://python.langchain.com/v0.2/docs/integrations/platforms/).
 - **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/).
+- **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/v0.2/docs/langserve/).
 
 ### Open-source libraries
-
 - **`langchain-core`**: Base abstractions and LangChain Expression Language.
 - **`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.
+- **[`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.
 
 ### Productionization:
-
 - **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
 ### Deployment:
+- **[LangServe](https://python.langchain.com/v0.2/docs/langserve/)**: A library for deploying LangChain chains as REST APIs.
 
-- **[LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
-
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_062024.svg "LangChain Architecture Overview")
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack.svg "LangChain Architecture Overview")
 
 ## 🧱 What can you build with LangChain?
 
@@ -81,17 +77,15 @@ For these applications, LangChain simplifies the entire application lifecycle:
 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?
-
 The main value props of the LangChain libraries are:
-
 1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
 2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
 
-Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
+Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones. 
 
 ## LangChain Expression Language (LCEL)
 
-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.
+LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
 
 - **[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
@@ -112,7 +106,7 @@ Retrieval Augmented Generation involves [loading data](https://python.langchain.
 
 **🤖 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.
+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 the [LangGraph](https://github.com/langchain-ai/langgraph) extension for building custom agents.
 
 ## 📖 Documentation
 
@@ -126,9 +120,11 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 ## 🌐 Ecosystem
 
-- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
-- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
-- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploy LangChain runnables and chains as REST APIs.
+- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
+- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
+- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs.
+  - [LangChain Templates](https://python.langchain.com/v0.2/docs/templates/): Example applications hosted with LangServe.
+
 
 ## 💁 Contributing
 

cookbook/Multi_modal_RAG.ipynb

@@ -64,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -U langchain openai langchain-chroma langchain-experimental # (newest versions required for multi-modal)"
+    "! pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)"
    ]
   },
   {
@@ -355,7 +355,7 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",

cookbook/Multi_modal_RAG_google.ipynb

@@ -37,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U --quiet langchain langchain-chroma langchain-community openai langchain-experimental\n",
+    "%pip install -U --quiet langchain langchain_community openai chromadb langchain-experimental\n",
     "%pip install --quiet \"unstructured[all-docs]\" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken"
    ]
   },
@@ -344,8 +344,8 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.embeddings import VertexAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "\n",
@@ -445,7 +445,7 @@
     "\n",
     "\n",
     "def plt_img_base64(img_base64):\n",
-    "    \"\"\"Display base64 encoded string as image\"\"\"\n",
+    "    \"\"\"Disply base64 encoded string as image\"\"\"\n",
     "    # Create an HTML img tag with the base64 string as the source\n",
     "    image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
     "    # Display the image by rendering the HTML\n",

cookbook/RAPTOR.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install -U langchain umap-learn scikit-learn langchain_community tiktoken langchain-openai langchainhub langchain-chroma langchain-anthropic"
+    "pip install -U langchain umap-learn scikit-learn langchain_community tiktoken langchain-openai langchainhub chromadb langchain-anthropic"
    ]
   },
   {
@@ -645,7 +645,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "\n",
     "# Initialize all_texts with leaf_texts\n",
     "all_texts = leaf_texts.copy()\n",

cookbook/README.md

@@ -4,8 +4,6 @@ Example code for building applications with LangChain, with an emphasis on more
 
 Notebook | Description
 :- | :-
-[agent_fireworks_ai_langchain_mongodb.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/agent_fireworks_ai_langchain_mongodb.ipynb) | Build an AI Agent With Memory Using MongoDB, LangChain and FireWorksAI.
-[mongodb-langchain-cache-memory.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/mongodb-langchain-cache-memory.ipynb) | Build a RAG Application with Semantic Cache Using MongoDB and LangChain.
 [LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters.
 [Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
 [Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.
@@ -38,7 +36,6 @@ Notebook | Description
 [llm_symbolic_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_symbolic_math.ipynb) | Solve algebraic equations with the help of llms (language learning models) and sympy, a python library for symbolic mathematics.
 [meta_prompt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/meta_prompt.ipynb) | Implement the meta-prompt concept, which is a method for building self-improving agents that reflect on their own performance and modify their instructions accordingly.
 [multi_modal_output_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_output_agent.ipynb) | Generate multi-modal outputs, specifically images and text.
-[multi_modal_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_RAG_vdms.ipynb) | Perform retrieval-augmented generation (rag) on documents including text and images, using unstructured for parsing, Intel's Visual Data Management System (VDMS) as the vectorstore, and chains.
 [multi_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_player_dnd.ipynb) | Simulate multi-player dungeons & dragons games, with a custom function determining the speaking schedule of the agents.
 [multiagent_authoritarian.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_authoritarian.ipynb) | Implement a multi-agent simulation where a privileged agent controls the conversation, including deciding who speaks and when the conversation ends, in the context of a simulated news network.
 [multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example.
@@ -60,6 +57,4 @@ Notebook | Description
 [two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools.
 [two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master.
 [wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org.
-[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.
-[rag-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag-locally-on-intel-cpu.ipynb) | Perform Retrieval-Augmented-Generation (RAG) on locally downloaded open-source models using langchain and open source tools and execute it on Intel Xeon CPU. We showed an example of how to apply RAG on Llama 2 model and enable it to answer the queries related to Intel Q1 2024 earnings release.
-[visual_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/visual_RAG_vdms.ipynb) | Performs Visual Retrieval-Augmented-Generation (RAG) using videos and scene descriptions generated by open source models.
+[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.

cookbook/Semi_Structured_RAG.ipynb

@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml langchainhub"
+    "! pip install langchain unstructured[all-docs] pydantic lxml langchainhub"
    ]
   },
   {
@@ -320,7 +320,7 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml"
+    "! pip install langchain unstructured[all-docs] pydantic lxml"
    ]
   },
   {
@@ -375,7 +375,7 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml"
+    "! pip install langchain unstructured[all-docs] pydantic lxml"
    ]
   },
   {
@@ -378,8 +378,8 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.embeddings import GPT4AllEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",

cookbook/advanced_rag_eval.ipynb

@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -U langchain openai langchain_chroma langchain-experimental # (newest versions required for multi-modal)"
+    "! pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)"
    ]
   },
   {
@@ -132,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "baseline = Chroma.from_texts(\n",

cookbook/agent_vectorstore.ipynb

@@ -28,7 +28,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import RetrievalQA\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import CharacterTextSplitter\n",
     "\n",

cookbook/airbyte_github.ipynb

@@ -14,7 +14,7 @@
     }
    ],
    "source": [
-    "%pip install -qU langchain-airbyte langchain_chroma"
+    "%pip install -qU langchain-airbyte"
    ]
   },
   {
@@ -123,7 +123,7 @@
    "outputs": [],
    "source": [
     "import tiktoken\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "enc = tiktoken.get_encoding(\"cl100k_base\")\n",

cookbook/cql_agent.ipynb

@@ -38,7 +38,7 @@
    "source": [
     "Connection is via `cassio` using `auto=True` parameter, and the notebook uses OpenAI. You should create a `.env` file accordingly.\n",
     "\n",
-    "For Cassandra, set:\n",
+    "For Casssandra, set:\n",
     "```bash\n",
     "CASSANDRA_CONTACT_POINTS\n",
     "CASSANDRA_USERNAME\n",

cookbook/databricks_sql_db.ipynb

@@ -166,7 +166,7 @@
    "source": [
     "### SQL Database Agent example\n",
     "\n",
-    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
+    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/toolkits/sql_database.html) for answering questions over a Databricks database."
    ]
   },
   {

cookbook/docugami_xml_kg_rag.ipynb

@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub langchain-chroma hnswlib --upgrade --quiet"
+    "! pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub chromadb hnswlib --upgrade --quiet"
    ]
   },
   {
@@ -547,7 +547,7 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores.chroma import Chroma\n",
     "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",

cookbook/fireworks_rag.ipynb

@@ -84,7 +84,7 @@
     }
    ],
    "source": [
-    "%pip install --quiet pypdf langchain-chroma tiktoken openai \n",
+    "%pip install --quiet pypdf chromadb tiktoken openai \n",
     "%pip uninstall -y langchain-fireworks\n",
     "%pip install --editable /mnt/disks/data/langchain/libs/partners/fireworks"
    ]
@@ -138,7 +138,7 @@
     "all_splits = text_splitter.split_documents(data)\n",
     "\n",
     "# Add to vectorDB\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_fireworks.embeddings import FireworksEmbeddings\n",
     "\n",
     "vectorstore = Chroma.from_documents(\n",

cookbook/hypothetical_document_embeddings.ipynb

@@ -170,7 +170,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_text_splitters import CharacterTextSplitter\n",
     "\n",
     "with open(\"../../state_of_the_union.txt\") as f:\n",

cookbook/langgraph_agentic_rag.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain-chroma langchain_community tiktoken langchain-openai langchainhub langchain langgraph"
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
    ]
   },
   {
@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "urls = [\n",

cookbook/langgraph_crag.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain-chroma langchain_community tiktoken langchain-openai langchainhub langchain langgraph tavily-python"
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python"
    ]
   },
   {
@@ -77,8 +77,8 @@
    "outputs": [],
    "source": [
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "urls = [\n",
@@ -180,8 +180,8 @@
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import Document\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.messages import BaseMessage, FunctionMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",

cookbook/langgraph_self_rag.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain-chroma langchain_community tiktoken langchain-openai langchainhub langchain langgraph"
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
    ]
   },
   {
@@ -86,8 +86,8 @@
    "outputs": [],
    "source": [
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "urls = [\n",
@@ -188,7 +188,7 @@
     "from langchain.output_parsers import PydanticOutputParser\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.messages import BaseMessage, FunctionMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
@@ -336,7 +336,7 @@
     "    # Create a prompt template with format instructions and the query\n",
     "    prompt = PromptTemplate(\n",
     "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
-    "        Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n",
+    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
     "        Here is the initial question:\n",
     "        \\n ------- \\n\n",
     "        {question} \n",
@@ -643,7 +643,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.1 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -657,12 +657,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
-   }
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

cookbook/multi_modal_RAG_chroma.ipynb

@@ -58,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -U langchain openai langchain-chroma langchain-experimental # (newest versions required for multi-modal)"
+    "! pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)"
    ]
   },
   {
@@ -187,7 +187,7 @@
     "\n",
     "import chromadb\n",
     "import numpy as np\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
     "from PIL import Image as _PILImage\n",
     "\n",

cookbook/multi_modal_RAG_vdms.ipynb

@@ -18,7 +18,26 @@
     "* Use of multimodal embeddings (such as [CLIP](https://openai.com/research/clip)) to embed images and text\n",
     "* Use of [VDMS](https://github.com/IntelLabs/vdms/blob/master/README.md) as a vector store with support for multi-modal\n",
     "* Retrieval of both images and text using similarity search\n",
-    "* Passing raw images and text chunks to a multimodal LLM for answer synthesis "
+    "* Passing raw images and text chunks to a multimodal LLM for answer synthesis \n",
+    "\n",
+    "\n",
+    "## Packages\n",
+    "\n",
+    "For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# (newest versions required for multi-modal)\n",
+    "! pip install --quiet -U vdms langchain-experimental\n",
+    "\n",
+    "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
+    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
    ]
   },
   {
@@ -34,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "5f483872",
    "metadata": {},
    "outputs": [
@@ -42,7 +61,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a1b9206b08ef626e15b356bf9e031171f7c7eb8f956a2733f196f0109246fe2b\n"
+      "docker: Error response from daemon: Conflict. The container name \"/vdms_rag_nb\" is already in use by container \"0c19ed281463ac10d7efe07eb815643e3e534ddf24844357039453ad2b0c27e8\". You have to remove (or rename) that container to be able to reuse that name.\n",
+      "See 'docker run --help'.\n"
      ]
     }
    ],
@@ -55,32 +75,9 @@
     "vdms_client = VDMS_Client(port=55559)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "2498a0a1",
-   "metadata": {},
-   "source": [
-    "## Packages\n",
-    "\n",
-    "For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install --quiet -U vdms langchain-experimental\n",
-    "\n",
-    "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
-    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "78ac6543",
    "metadata": {},
    "outputs": [],
@@ -98,9 +95,14 @@
     "\n",
     "### Partition PDF text and images\n",
     "  \n",
-    "Let's use famous photographs from the PDF version of Library of Congress Magazine in this example.\n",
+    "Let's look at an example pdf containing interesting images.\n",
     "\n",
-    "We can use `partition_pdf` from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images."
+    "Famous photographs from library of congress:\n",
+    "\n",
+    "* https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\n",
+    "* We'll use this as an example below\n",
+    "\n",
+    "We can use `partition_pdf` below from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images."
    ]
   },
   {
@@ -114,8 +116,8 @@
     "\n",
     "import requests\n",
     "\n",
-    "# Folder to store pdf and extracted images\n",
-    "datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "# Folder with pdf and extracted images\n",
+    "datapath = Path(\"./multimodal_files\").resolve()\n",
     "datapath.mkdir(parents=True, exist_ok=True)\n",
     "\n",
     "pdf_url = \"https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\"\n",
@@ -172,8 +174,14 @@
    "source": [
     "## Multi-modal embeddings with our document\n",
     "\n",
-    "In this section, we initialize the VDMS vector store for both text and images. For better performance, we use model `ViT-g-14` from [OpenClip multimodal embeddings](https://python.langchain.com/docs/integrations/text_embedding/open_clip).\n",
-    "The images are stored as base64 encoded strings with `vectorstore.add_images`.\n"
+    "We will use [OpenClip multimodal embeddings](https://python.langchain.com/docs/integrations/text_embedding/open_clip).\n",
+    "\n",
+    "We use a larger model for better performance (set in `langchain_experimental.open_clip.py`).\n",
+    "\n",
+    "```\n",
+    "model_name = \"ViT-g-14\"\n",
+    "checkpoint = \"laion2b_s34b_b88k\"\n",
+    "```"
    ]
   },
   {
@@ -192,7 +200,9 @@
     "vectorstore = VDMS(\n",
     "    client=vdms_client,\n",
     "    collection_name=\"mm_rag_clip_photos\",\n",
-    "    embedding=OpenCLIPEmbeddings(model_name=\"ViT-g-14\", checkpoint=\"laion2b_s34b_b88k\"),\n",
+    "    embedding_function=OpenCLIPEmbeddings(\n",
+    "        model_name=\"ViT-g-14\", checkpoint=\"laion2b_s34b_b88k\"\n",
+    "    ),\n",
     ")\n",
     "\n",
     "# Get image URIs with .jpg extension only\n",
@@ -223,7 +233,7 @@
    "source": [
     "## RAG\n",
     "\n",
-    "Here we define helper functions for image results."
+    "`vectorstore.add_images` will store / retrieve images as base64 encoded strings."
    ]
   },
   {
@@ -382,8 +392,7 @@
    "id": "1566096d-97c2-4ddc-ba4a-6ef88c525e4e",
    "metadata": {},
    "source": [
-    "## Test retrieval and run RAG\n",
-    "Now let's query for a `woman with children` and retrieve the top results."
+    "## Test retrieval and run RAG"
    ]
   },
   {
@@ -443,14 +452,6 @@
     "        print(doc.page_content)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "15e9b54d",
-   "metadata": {},
-   "source": [
-    "Now let's use the `multi_modal_rag_chain` to process the same query and display the response."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -461,10 +462,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The image depicts a woman with several children. The woman appears to be of Cherokee heritage, as suggested by the text provided. The image is described as having been initially regretted by the subject, Florence Owens Thompson, due to her feeling that it did not accurately represent her leadership qualities.\n",
-      "The historical and cultural context of the image is tied to the Great Depression and the Dust Bowl, both of which affected the Cherokee people in Oklahoma. The photograph was taken during this period, and its subject, Florence Owens Thompson, was a leader within her community who worked tirelessly to help those affected by these crises.\n",
-      "The image's symbolism and meaning can be interpreted as a representation of resilience and strength in the face of adversity. The woman is depicted with multiple children, which could signify her role as a caregiver and protector during difficult times.\n",
-      "Connections between the image and the related text include Florence Owens Thompson's leadership qualities and her regretted feelings about the photograph. Additionally, the mention of Dorothea Lange, the photographer who took this photo, ties the image to its historical context and the broader narrative of the Great Depression and Dust Bowl in Oklahoma. \n"
+      "1. Detailed description of the visual elements in the image: The image features a woman with children, likely a mother and her family, standing together outside. They appear to be poor or struggling financially, as indicated by their attire and surroundings.\n",
+      "2. Historical and cultural context of the image: The photo was taken in 1936 during the Great Depression, when many families struggled to make ends meet. Dorothea Lange, a renowned American photographer, took this iconic photograph that became an emblem of poverty and hardship experienced by many Americans at that time.\n",
+      "3. Interpretation of the image's symbolism and meaning: The image conveys a sense of unity and resilience despite adversity. The woman and her children are standing together, displaying their strength as a family unit in the face of economic challenges. The photograph also serves as a reminder of the importance of empathy and support for those who are struggling.\n",
+      "4. Connections between the image and the related text: The text provided offers additional context about the woman in the photo, her background, and her feelings towards the photograph. It highlights the historical backdrop of the Great Depression and emphasizes the significance of this particular image as a representation of that time period.\n"
      ]
     }
    ],
@@ -491,6 +492,14 @@
    "source": [
     "! docker kill vdms_rag_nb"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ba652da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -509,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

cookbook/nomic_embedding_rag.ipynb

@@ -58,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -U langchain-nomic langchain-chroma langchain-community tiktoken langchain-openai langchain"
+    "! pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain"
    ]
   },
   {
@@ -167,7 +167,7 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "from langchain_nomic import NomicEmbeddings\n",

cookbook/nomic_multimodal_rag.ipynb

@@ -56,7 +56,7 @@
    },
    "outputs": [],
    "source": [
-    "! pip install -U langchain-nomic langchain-chroma langchain-community tiktoken langchain-openai langchain # (newest versions required for multi-modal)"
+    "! pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain # (newest versions required for multi-modal)"
    ]
   },
   {
@@ -194,7 +194,7 @@
     "\n",
     "import chromadb\n",
     "import numpy as np\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_nomic import NomicEmbeddings\n",
     "from PIL import Image as _PILImage\n",
     "\n",

cookbook/openai_functions_retrieval_qa.ipynb

@@ -20,8 +20,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import RetrievalQA\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import CharacterTextSplitter"
    ]

cookbook/optimization.ipynb

@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_chroma import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"

cookbook/rag-locally-on-intel-cpu.ipynb

@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "10f50955-be55-422f-8c62-3a32f8cf02ed",
-   "metadata": {},
-   "source": [
-    "# RAG application running locally on Intel Xeon CPU using langchain and open-source models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48113be6-44bb-4aac-aed3-76a1365b9561",
-   "metadata": {},
-   "source": [
-    "Author - Pratool Bharti (pratool.bharti@intel.com)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b10b54b-1572-4ea1-9c1e-1d29fcc3dcd9",
-   "metadata": {},
-   "source": [
-    "In this cookbook, we use langchain tools and open source models to execute locally on CPU. This notebook has been validated to run on Intel Xeon 8480+ CPU. Here we implement a RAG pipeline for Llama2 model to answer questions about Intel Q1 2024 earnings release."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acadbcec-3468-4926-8ce5-03b678041c0a",
-   "metadata": {},
-   "source": [
-    "**Create a conda or virtualenv environment with python >=3.10 and install following libraries**\n",
-    "<br>\n",
-    "\n",
-    "`pip install --upgrade langchain langchain-community langchainhub langchain-chroma bs4 gpt4all pypdf pysqlite3-binary` <br>\n",
-    "`pip install llama-cpp-python   --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84c392c8-700a-42ec-8e94-806597f22e43",
-   "metadata": {},
-   "source": [
-    "**Load pysqlite3 in sys modules since ChromaDB requires sqlite3.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "145cd491-b388-4ea7-bdc8-2f4995cac6fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "__import__(\"pysqlite3\")\n",
-    "import sys\n",
-    "\n",
-    "sys.modules[\"sqlite3\"] = sys.modules.pop(\"pysqlite3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14dde7e2-b236-49b9-b3a0-08c06410418c",
-   "metadata": {},
-   "source": [
-    "**Import essential components from langchain to load and split data**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "887643ba-249e-48d6-9aa7-d25087e8dfbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import PyPDFLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "922c0eba-8736-4de5-bd2f-3d0f00b16e43",
-   "metadata": {},
-   "source": [
-    "**Download Intel Q1 2024 earnings release**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2d6a2419-5338-4188-8615-a40a65ff8019",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2024-07-15 15:04:43--  https://d1io3yog0oux5.cloudfront.net/_11d435a500963f99155ee058df09f574/intel/db/887/9014/earnings_release/Q1+24_EarningsRelease_FINAL.pdf\n",
-      "Resolving proxy-dmz.intel.com (proxy-dmz.intel.com)... 10.7.211.16\n",
-      "Connecting to proxy-dmz.intel.com (proxy-dmz.intel.com)|10.7.211.16|:912... connected.\n",
-      "Proxy request sent, awaiting response... 200 OK\n",
-      "Length: 133510 (130K) [application/pdf]\n",
-      "Saving to: ‘intel_q1_2024_earnings.pdf’\n",
-      "\n",
-      "intel_q1_2024_earni 100%[===================>] 130.38K  --.-KB/s    in 0.005s  \n",
-      "\n",
-      "2024-07-15 15:04:44 (24.6 MB/s) - ‘intel_q1_2024_earnings.pdf’ saved [133510/133510]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget  'https://d1io3yog0oux5.cloudfront.net/_11d435a500963f99155ee058df09f574/intel/db/887/9014/earnings_release/Q1+24_EarningsRelease_FINAL.pdf' -O intel_q1_2024_earnings.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3612627-e105-453d-8a50-bbd6e39dedb5",
-   "metadata": {},
-   "source": [
-    "**Loading earning release pdf document through PyPDFLoader**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "cac6278e-ebad-4224-a062-bf6daca24cb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = PyPDFLoader(\"intel_q1_2024_earnings.pdf\")\n",
-    "data = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7dca43b-1c62-41df-90c7-6ed2904f823d",
-   "metadata": {},
-   "source": [
-    "**Splitting entire document in several chunks with each chunk size is 500 tokens**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4486adbe-0d0e-4685-8c08-c1774ed6e993",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af142346-e793-4a52-9a56-63e3be416b3d",
-   "metadata": {},
-   "source": [
-    "**Looking at the first split of the document**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e4240fd1-898e-4bfc-a377-02c9bc25b56e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': 'intel_q1_2024_earnings.pdf', 'page': 0}, page_content='Intel Corporation\\n2200 Mission College Blvd.\\nSanta Clara, CA 95054-1549\\n                                                         \\nNews Release\\n Intel Reports First -Quarter 2024  Financial Results\\nNEWS SUMMARY\\n▪First-quarter revenue of $12.7 billion , up 9%  year over year (YoY).\\n▪First-quarter GAAP earnings (loss) per share (EPS) attributable to Intel was $(0.09) ; non-GAAP EPS \\nattributable to Intel was $0.18 .')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_splits[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b88d2632-7c1b-49ef-a691-c0eb67d23e6a",
-   "metadata": {},
-   "source": [
-    "**One of the major step in RAG is to convert each split of document into embeddings and store in a vector database such that searching relevant documents are efficient.** <br>\n",
-    "**For that, importing Chroma vector database from langchain. Also, importing open source GPT4All for embedding models**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9ff99dd7-9d47-4239-ba0a-d775792334ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_chroma import Chroma\n",
-    "from langchain_community.embeddings import GPT4AllEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5d1f4dd-dd8d-4a20-95d1-2dbdd204375a",
-   "metadata": {},
-   "source": [
-    "**In next step, we will download one of the most popular embedding model \"all-MiniLM-L6-v2\". Find more details of the model at this link https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "05db3494-5d8e-4a13-9941-26330a86f5e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = \"all-MiniLM-L6-v2.gguf2.f16.gguf\"\n",
-    "gpt4all_kwargs = {\"allow_download\": \"True\"}\n",
-    "embeddings = GPT4AllEmbeddings(model_name=model_name, gpt4all_kwargs=gpt4all_kwargs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e53999e-1983-46ac-8039-2783e194c3ae",
-   "metadata": {},
-   "source": [
-    "**Store all the embeddings in the Chroma database**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0922951a-9ddf-4761-973d-8e9a86f61284",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=embeddings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29f94fa0-6c75-4a65-a1a3-debc75422479",
-   "metadata": {},
-   "source": [
-    "**Now, let's find relevant splits from the documents related to the question**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "88c8152d-ec7a-4f0b-9d86-877789407537",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "question = \"What is Intel CCG revenue in Q1 2024\"\n",
-    "docs = vectorstore.similarity_search(question)\n",
-    "print(len(docs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53330c6b-cb0f-43f9-b379-2e57ac1e5335",
-   "metadata": {},
-   "source": [
-    "**Look at the first retrieved document from the vector database**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "43a6d94f-b5c4-47b0-a353-2db4c3d24d9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'page': 1, 'source': 'intel_q1_2024_earnings.pdf'}, page_content='Client Computing Group (CCG) $7.5 billion up31%\\nData Center and AI (DCAI) $3.0 billion up5%\\nNetwork and Edge (NEX) $1.4 billion down 8%\\nTotal Intel Products revenue $11.9 billion up17%\\nIntel Foundry $4.4 billion down 10%\\nAll other:\\nAltera $342 million down 58%\\nMobileye $239 million down 48%\\nOther $194 million up17%\\nTotal all other revenue $775 million down 46%\\nIntersegment eliminations $(4.4) billion\\nTotal net revenue $12.7 billion up9%\\nIntel Products Highlights')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64ba074f-4b36-442e-b7e2-b26d6e2815c3",
-   "metadata": {},
-   "source": [
-    "**Download Lllama-2 model from Huggingface and store locally** <br>\n",
-    "**You can download different quantization variant of Lllama-2 model from the link below. We are using Q8 version here (7.16GB).** <br>\n",
-    "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8dd0811-6f43-4bc6-b854-2ab377639c9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF llama-2-7b-chat.Q8_0.gguf --local-dir . --local-dir-use-symlinks False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3895b1f5-f51d-4539-abf0-af33d7ca48ea",
-   "metadata": {},
-   "source": [
-    "**Import langchain components required to load downloaded LLMs model**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "fb087088-aa62-44c0-8356-061e9b9f1186",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain_community.llms import LlamaCpp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a8a111e-2614-4b70-b034-85cd3e7304cb",
-   "metadata": {},
-   "source": [
-    "**Loading the local Lllama-2 model using Llama-cpp library**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "fb917da2-c0d7-4995-b56d-26254276e0da",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from llama-2-7b-chat.Q8_0.gguf (version GGUF V2)\n",
-      "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
-      "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
-      "llama_model_loader: - kv   1:                               general.name str              = LLaMA v2\n",
-      "llama_model_loader: - kv   2:                       llama.context_length u32              = 4096\n",
-      "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096\n",
-      "llama_model_loader: - kv   4:                          llama.block_count u32              = 32\n",
-      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 11008\n",
-      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
-      "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32\n",
-      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 32\n",
-      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000001\n",
-      "llama_model_loader: - kv  10:                          general.file_type u32              = 7\n",
-      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama\n",
-      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
-      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
-      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
-      "llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1\n",
-      "llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2\n",
-      "llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0\n",
-      "llama_model_loader: - kv  18:               general.quantization_version u32              = 2\n",
-      "llama_model_loader: - type  f32:   65 tensors\n",
-      "llama_model_loader: - type q8_0:  226 tensors\n",
-      "llm_load_vocab: special tokens cache size = 259\n",
-      "llm_load_vocab: token to piece cache size = 0.1684 MB\n",
-      "llm_load_print_meta: format           = GGUF V2\n",
-      "llm_load_print_meta: arch             = llama\n",
-      "llm_load_print_meta: vocab type       = SPM\n",
-      "llm_load_print_meta: n_vocab          = 32000\n",
-      "llm_load_print_meta: n_merges         = 0\n",
-      "llm_load_print_meta: vocab_only       = 0\n",
-      "llm_load_print_meta: n_ctx_train      = 4096\n",
-      "llm_load_print_meta: n_embd           = 4096\n",
-      "llm_load_print_meta: n_layer          = 32\n",
-      "llm_load_print_meta: n_head           = 32\n",
-      "llm_load_print_meta: n_head_kv        = 32\n",
-      "llm_load_print_meta: n_rot            = 128\n",
-      "llm_load_print_meta: n_swa            = 0\n",
-      "llm_load_print_meta: n_embd_head_k    = 128\n",
-      "llm_load_print_meta: n_embd_head_v    = 128\n",
-      "llm_load_print_meta: n_gqa            = 1\n",
-      "llm_load_print_meta: n_embd_k_gqa     = 4096\n",
-      "llm_load_print_meta: n_embd_v_gqa     = 4096\n",
-      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
-      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-06\n",
-      "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
-      "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
-      "llm_load_print_meta: f_logit_scale    = 0.0e+00\n",
-      "llm_load_print_meta: n_ff             = 11008\n",
-      "llm_load_print_meta: n_expert         = 0\n",
-      "llm_load_print_meta: n_expert_used    = 0\n",
-      "llm_load_print_meta: causal attn      = 1\n",
-      "llm_load_print_meta: pooling type     = 0\n",
-      "llm_load_print_meta: rope type        = 0\n",
-      "llm_load_print_meta: rope scaling     = linear\n",
-      "llm_load_print_meta: freq_base_train  = 10000.0\n",
-      "llm_load_print_meta: freq_scale_train = 1\n",
-      "llm_load_print_meta: n_ctx_orig_yarn  = 4096\n",
-      "llm_load_print_meta: rope_finetuned   = unknown\n",
-      "llm_load_print_meta: ssm_d_conv       = 0\n",
-      "llm_load_print_meta: ssm_d_inner      = 0\n",
-      "llm_load_print_meta: ssm_d_state      = 0\n",
-      "llm_load_print_meta: ssm_dt_rank      = 0\n",
-      "llm_load_print_meta: model type       = 7B\n",
-      "llm_load_print_meta: model ftype      = Q8_0\n",
-      "llm_load_print_meta: model params     = 6.74 B\n",
-      "llm_load_print_meta: model size       = 6.67 GiB (8.50 BPW) \n",
-      "llm_load_print_meta: general.name     = LLaMA v2\n",
-      "llm_load_print_meta: BOS token        = 1 '<s>'\n",
-      "llm_load_print_meta: EOS token        = 2 '</s>'\n",
-      "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
-      "llm_load_print_meta: LF token         = 13 '<0x0A>'\n",
-      "llm_load_print_meta: max token length = 48\n",
-      "llm_load_tensors: ggml ctx size =    0.14 MiB\n",
-      "llm_load_tensors:        CPU buffer size =  6828.64 MiB\n",
-      "...................................................................................................\n",
-      "llama_new_context_with_model: n_ctx      = 2048\n",
-      "llama_new_context_with_model: n_batch    = 512\n",
-      "llama_new_context_with_model: n_ubatch   = 512\n",
-      "llama_new_context_with_model: flash_attn = 0\n",
-      "llama_new_context_with_model: freq_base  = 10000.0\n",
-      "llama_new_context_with_model: freq_scale = 1\n",
-      "llama_kv_cache_init:        CPU KV buffer size =  1024.00 MiB\n",
-      "llama_new_context_with_model: KV self size  = 1024.00 MiB, K (f16):  512.00 MiB, V (f16):  512.00 MiB\n",
-      "llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB\n",
-      "llama_new_context_with_model:        CPU compute buffer size =   164.01 MiB\n",
-      "llama_new_context_with_model: graph nodes  = 1030\n",
-      "llama_new_context_with_model: graph splits = 1\n",
-      "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | \n",
-      "Model metadata: {'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '11008', 'llama.attention.layer_norm_rms_epsilon': '0.000001', 'llama.rope.dimension_count': '128', 'llama.attention.head_count': '32', 'tokenizer.ggml.bos_token_id': '1', 'llama.block_count': '32', 'llama.attention.head_count_kv': '32', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '7'}\n",
-      "Using fallback chat format: llama-2\n"
-     ]
-    }
-   ],
-   "source": [
-    "llm = LlamaCpp(\n",
-    "    model_path=\"llama-2-7b-chat.Q8_0.gguf\",\n",
-    "    n_gpu_layers=-1,\n",
-    "    n_batch=512,\n",
-    "    n_ctx=2048,\n",
-    "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43e06f56-ef97-451b-87d9-8465ea442aed",
-   "metadata": {},
-   "source": [
-    "**Now let's ask the same question to Llama model without showing them the earnings release.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1033dd82-5532-437d-a548-27695e109589",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "?\n",
-      "(NASDAQ:INTC)\n",
-      "Intel's CCG (Client Computing Group) revenue for Q1 2024 was $9.6 billion, a decrease of 35% from the previous quarter and a decrease of 42% from the same period last year."
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "llama_print_timings:        load time =     131.20 ms\n",
-      "llama_print_timings:      sample time =      16.05 ms /    68 runs   (    0.24 ms per token,  4236.76 tokens per second)\n",
-      "llama_print_timings: prompt eval time =     131.14 ms /    16 tokens (    8.20 ms per token,   122.01 tokens per second)\n",
-      "llama_print_timings:        eval time =    3225.00 ms /    67 runs   (   48.13 ms per token,    20.78 tokens per second)\n",
-      "llama_print_timings:       total time =    3466.40 ms /    83 tokens\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"?\\n(NASDAQ:INTC)\\nIntel's CCG (Client Computing Group) revenue for Q1 2024 was $9.6 billion, a decrease of 35% from the previous quarter and a decrease of 42% from the same period last year.\""
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm.invoke(question)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75f5cb10-746f-4e37-9386-b85a4d2b84ef",
-   "metadata": {},
-   "source": [
-    "**As you can see, model is giving wrong information. Correct asnwer is CCG revenue in Q1 2024 is $7.5B. Now let's apply RAG using the earning release document**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f4150ec-5692-4756-b11a-22feb7ab88ff",
-   "metadata": {},
-   "source": [
-    "**in RAG, we modify the input prompt by adding relevent documents with the question. Here, we use one of the popular RAG prompt**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "226c14b0-f43e-4a1f-a1e4-04731d467ec4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: {question} \\nContext: {context} \\nAnswer:\"))]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "\n",
-    "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "rag_prompt.messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77deb6a0-0950-450a-916a-f2a029676c20",
-   "metadata": {},
-   "source": [
-    "**Appending all retreived documents in a single document**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "2dbc3327-6ef3-4c1f-8797-0c71964b0921",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2e2d9f18-49d0-43a3-bea8-78746ffa86b7",
-   "metadata": {},
-   "source": [
-    "**The last step is to create a chain using langchain tool that will create an e2e pipeline. It will take question and context as an input.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "427379c2-51ff-4e0f-8278-a45221363299",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough, RunnablePick\n",
-    "\n",
-    "# Chain\n",
-    "chain = (\n",
-    "    RunnablePassthrough.assign(context=RunnablePick(\"context\") | format_docs)\n",
-    "    | rag_prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "095d6280-c949-4d00-8e32-8895a82d245f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Llama.generate: prefix-match hit\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Based on the provided context, Intel CCG revenue in Q1 2024 was $7.5 billion up 31%."
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "llama_print_timings:        load time =     131.20 ms\n",
-      "llama_print_timings:      sample time =       7.74 ms /    31 runs   (    0.25 ms per token,  4004.13 tokens per second)\n",
-      "llama_print_timings: prompt eval time =    2529.41 ms /   674 tokens (    3.75 ms per token,   266.46 tokens per second)\n",
-      "llama_print_timings:        eval time =    1542.94 ms /    30 runs   (   51.43 ms per token,    19.44 tokens per second)\n",
-      "llama_print_timings:       total time =    4123.68 ms /   704 tokens\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' Based on the provided context, Intel CCG revenue in Q1 2024 was $7.5 billion up 31%.'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"context\": docs, \"question\": question})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "638364b2-6bd2-4471-9961-d3a1d1b9d4ee",
-   "metadata": {},
-   "source": [
-    "**Now we see the results are correct as it is mentioned in earnings release.** <br>\n",
-    "**To further automate, we will create a chain that will take input as question and retriever so that we don't need to retrieve documents separately**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "4654e5b7-635f-4767-8b31-4c430164cdd5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vectorstore.as_retriever()\n",
-    "qa_chain = (\n",
-    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | rag_prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0979f393-fd0a-4e82-b844-68371c6ad68f",
-   "metadata": {},
-   "source": [
-    "**Now we only need to pass the question to the chain and it will fetch the contexts directly from the vector database to generate the answer**\n",
-    "<br>\n",
-    "**Let's try with another question**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "3ea07b82-e6ec-4084-85f4-191373530172",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Llama.generate: prefix-match hit\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " According to the provided context, Intel DCAI revenue in Q1 2024 was $3.0 billion up 5%."
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "llama_print_timings:        load time =     131.20 ms\n",
-      "llama_print_timings:      sample time =       6.28 ms /    31 runs   (    0.20 ms per token,  4937.88 tokens per second)\n",
-      "llama_print_timings: prompt eval time =    2681.93 ms /   730 tokens (    3.67 ms per token,   272.19 tokens per second)\n",
-      "llama_print_timings:        eval time =    1471.07 ms /    30 runs   (   49.04 ms per token,    20.39 tokens per second)\n",
-      "llama_print_timings:       total time =    4206.77 ms /   760 tokens\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' According to the provided context, Intel DCAI revenue in Q1 2024 was $3.0 billion up 5%.'"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qa_chain.invoke(\"what is Intel DCAI revenue in Q1 2024?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9407f2a0-4a35-4315-8e96-02fcb80f210c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.11.1 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/rag_with_quantized_embeddings.ipynb

@@ -36,10 +36,10 @@
     "from bs4 import BeautifulSoup as Soup\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryByteStore, LocalFileStore\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders.recursive_url_loader import (\n",
     "    RecursiveUrlLoader,\n",
     ")\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "\n",
     "# For our example, we'll load docs from the web\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
@@ -370,14 +370,13 @@
    ],
    "source": [
     "import torch\n",
-    "from langchain_huggingface.llms import HuggingFacePipeline\n",
-    "from optimum.intel.ipex import IPEXModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
+    "from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
     "\n",
     "model_id = \"Intel/neural-chat-7b-v3-3\"\n",
     "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-    "model = IPEXModelForCausalLM.from_pretrained(\n",
-    "    model_id, torch_dtype=torch.bfloat16, export=True\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n",
     ")\n",
     "\n",
     "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n",
@@ -582,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

cookbook/sql_db_qa.mdx

@@ -740,7 +740,7 @@ Even this relatively large model will most likely fail to generate more complica
 
 
 ```bash
-poetry run pip install pyyaml langchain_chroma
+poetry run pip install pyyaml chromadb
 import yaml
 ```
 
@@ -994,7 +994,7 @@ from langchain.prompts import FewShotPromptTemplate, PromptTemplate
 from langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIX
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
-from langchain_chroma import Chroma
+from langchain_community.vectorstores import Chroma
 
 example_prompt = PromptTemplate(
     input_variables=["table_info", "input", "sql_cmd", "sql_result", "answer"],

cookbook/together_ai.ipynb

@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install --quiet pypdf tiktoken openai langchain-chroma langchain-together"
+    "! pip install --quiet pypdf chromadb tiktoken openai langchain-together"
    ]
   },
   {
@@ -45,8 +45,8 @@
     "all_splits = text_splitter.split_documents(data)\n",
     "\n",
     "# Add to vectorDB\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "\n",
     "\"\"\"\n",
     "from langchain_together.embeddings import TogetherEmbeddings\n",

docs/Makefile

@@ -13,12 +13,7 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec sh -c ' \
-for dir; do \
-    if find "$$dir" -maxdepth 1 -type f \( -name "pyproject.toml" -o -name "setup.py" \) | grep -q .; then \
-        echo "$$dir"; \
-    fi \
-done' sh {} + | grep -vE "airbyte|ibm|couchbase|databricks" | tr '\n' ' ')
+PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm" | tr '\n' ' ')
 
 PORT ?= 3001
 
@@ -39,12 +34,11 @@ install-py-deps:
 generate-files:
 	mkdir -p $(INTERMEDIATE_DIR)
 	cp -r $(SOURCE_DIR)/* $(INTERMEDIATE_DIR)
+	mkdir -p $(INTERMEDIATE_DIR)/templates
 
-	$(PYTHON) scripts/tool_feat_table.py $(INTERMEDIATE_DIR)
+	$(PYTHON) scripts/model_feat_table.py $(INTERMEDIATE_DIR)
 
-	$(PYTHON) scripts/kv_store_feat_table.py $(INTERMEDIATE_DIR)
-
-	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
+	$(PYTHON) scripts/copy_templates.py $(INTERMEDIATE_DIR)
 
 	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/
@@ -65,27 +59,18 @@ render:
 	$(PYTHON) scripts/notebook_convert.py $(INTERMEDIATE_DIR) $(OUTPUT_NEW_DOCS_DIR)
 
 md-sync:
-	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)
+	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
 generate-references:
 	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)
 
-update-md: generate-files md-sync
-
-build: install-py-deps generate-files copy-infra render md-sync append-related
+build: install-py-deps generate-files copy-infra render md-sync
 
 vercel-build: install-vercel-deps build generate-references
 	rm -rf docs
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
-	mkdir static/api_reference
-	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
+	yarn run docusaurus build
 	mv build v0.2
 	mkdir build
 	mv v0.2 build

docs/api_reference/_extensions/gallery_directive.py

@@ -1,144 +0,0 @@
-"""A directive to generate a gallery of images from structured data.
-
-Generating a gallery of images that are all the same size is a common
-pattern in documentation, and this can be cumbersome if the gallery is
-generated programmatically. This directive wraps this particular use-case
-in a helper-directive to generate it with a single YAML configuration file.
-
-It currently exists for maintainers of the pydata-sphinx-theme,
-but might be abstracted into a standalone package if it proves useful.
-"""
-
-from pathlib import Path
-from typing import Any, ClassVar, Dict, List
-
-from docutils import nodes
-from docutils.parsers.rst import directives
-from sphinx.application import Sphinx
-from sphinx.util import logging
-from sphinx.util.docutils import SphinxDirective
-from yaml import safe_load
-
-logger = logging.getLogger(__name__)
-
-
-TEMPLATE_GRID = """
-`````{{grid}} {columns}
-{options}
-
-{content}
-
-`````
-"""
-
-GRID_CARD = """
-````{{grid-item-card}} {title}
-{options}
-
-{content}
-````
-"""
-
-
-class GalleryGridDirective(SphinxDirective):
-    """A directive to show a gallery of images and links in a Bootstrap grid.
-
-    The grid can be generated from a YAML file that contains a list of items, or
-    from the content of the directive (also formatted in YAML). Use the parameter
-    "class-card" to add an additional CSS class to all cards. When specifying the grid
-    items, you can use all parameters from "grid-item-card" directive to customize
-    individual cards + ["image", "header", "content", "title"].
-
-    Danger:
-        This directive can only be used in the context of a Myst documentation page as
-        the templates use Markdown flavored formatting.
-    """
-
-    name = "gallery-grid"
-    has_content = True
-    required_arguments = 0
-    optional_arguments = 1
-    final_argument_whitespace = True
-    option_spec: ClassVar[dict[str, Any]] = {
-        # A class to be added to the resulting container
-        "grid-columns": directives.unchanged,
-        "class-container": directives.unchanged,
-        "class-card": directives.unchanged,
-    }
-
-    def run(self) -> List[nodes.Node]:
-        """Create the gallery grid."""
-        if self.arguments:
-            # If an argument is given, assume it's a path to a YAML file
-            # Parse it and load it into the directive content
-            path_data_rel = Path(self.arguments[0])
-            path_doc, _ = self.get_source_info()
-            path_doc = Path(path_doc).parent
-            path_data = (path_doc / path_data_rel).resolve()
-            if not path_data.exists():
-                logger.info(f"Could not find grid data at {path_data}.")
-                nodes.text("No grid data found at {path_data}.")
-                return
-            yaml_string = path_data.read_text()
-        else:
-            yaml_string = "\n".join(self.content)
-
-        # Use all the element with an img-bottom key as sites to show
-        # and generate a card item for each of them
-        grid_items = []
-        for item in safe_load(yaml_string):
-            # remove parameters that are not needed for the card options
-            title = item.pop("title", "")
-
-            # build the content of the card using some extra parameters
-            header = f"{item.pop('header')}  \n^^^  \n" if "header" in item else ""
-            image = f"![image]({item.pop('image')})  \n" if "image" in item else ""
-            content = f"{item.pop('content')}  \n" if "content" in item else ""
-
-            # optional parameter that influence all cards
-            if "class-card" in self.options:
-                item["class-card"] = self.options["class-card"]
-
-            loc_options_str = "\n".join(f":{k}: {v}" for k, v in item.items()) + "  \n"
-
-            card = GRID_CARD.format(
-                options=loc_options_str, content=header + image + content, title=title
-            )
-            grid_items.append(card)
-
-        # Parse the template with Sphinx Design to create an output container
-        # Prep the options for the template grid
-        class_ = "gallery-directive" + f' {self.options.get("class-container", "")}'
-        options = {"gutter": 2, "class-container": class_}
-        options_str = "\n".join(f":{k}: {v}" for k, v in options.items())
-
-        # Create the directive string for the grid
-        grid_directive = TEMPLATE_GRID.format(
-            columns=self.options.get("grid-columns", "1 2 3 4"),
-            options=options_str,
-            content="\n".join(grid_items),
-        )
-
-        # Parse content as a directive so Sphinx Design processes it
-        container = nodes.container()
-        self.state.nested_parse([grid_directive], 0, container)
-
-        # Sphinx Design outputs a container too, so just use that
-        return [container.children[0]]
-
-
-def setup(app: Sphinx) -> Dict[str, Any]:
-    """Add custom configuration to sphinx app.
-
-    Args:
-        app: the Sphinx application
-
-    Returns:
-        the 2 parallel parameters set to ``True``.
-    """
-    app.add_directive("gallery-grid", GalleryGridDirective)
-
-    return {
-        "parallel_read_safe": True,
-        "parallel_write_safe": True,
-    }

docs/api_reference/_static/css/custom.css

@@ -1,411 +1,26 @@
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap');
-
-/*******************************************************************************
-* master color map. Only the colors that actually differ between light and dark
-* themes are specified separately.
-*
-* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1
-*/
-/**
-* Function to get items from nested maps
-*/
-/* Assign base colors for the PyData theme */
-:root {
-  --pst-teal-50: #f4fbfc;
-  --pst-teal-100: #e9f6f8;
-  --pst-teal-200: #d0ecf1;
-  --pst-teal-300: #abdde6;
-  --pst-teal-400: #3fb1c5;
-  --pst-teal-500: #0a7d91;
-  --pst-teal-600: #085d6c;
-  --pst-teal-700: #064752;
-  --pst-teal-800: #042c33;
-  --pst-teal-900: #021b1f;
-  --pst-violet-50: #f4eefb;
-  --pst-violet-100: #e0c7ff;
-  --pst-violet-200: #d5b4fd;
-  --pst-violet-300: #b780ff;
-  --pst-violet-400: #9c5ffd;
-  --pst-violet-500: #8045e5;
-  --pst-violet-600: #6432bd;
-  --pst-violet-700: #4b258f;
-  --pst-violet-800: #341a61;
-  --pst-violet-900: #1e0e39;
-  --pst-gray-50: #f9f9fa;
-  --pst-gray-100: #f3f4f5;
-  --pst-gray-200: #e5e7ea;
-  --pst-gray-300: #d1d5da;
-  --pst-gray-400: #9ca4af;
-  --pst-gray-500: #677384;
-  --pst-gray-600: #48566b;
-  --pst-gray-700: #29313d;
-  --pst-gray-800: #222832;
-  --pst-gray-900: #14181e;
-  --pst-pink-50: #fcf8fd;
-  --pst-pink-100: #fcf0fa;
-  --pst-pink-200: #f8dff5;
-  --pst-pink-300: #f3c7ee;
-  --pst-pink-400: #e47fd7;
-  --pst-pink-500: #c132af;
-  --pst-pink-600: #912583;
-  --pst-pink-700: #6e1c64;
-  --pst-pink-800: #46123f;
-  --pst-pink-900: #2b0b27;
-  --pst-foundation-white: #ffffff;
-  --pst-foundation-black: #14181e;
-  --pst-green-10: #f1fdfd;
-  --pst-green-50: #E0F7F6;
-  --pst-green-100: #B3E8E6;
-  --pst-green-200: #80D6D3;
-  --pst-green-300: #4DC4C0;
-  --pst-green-400: #4FB2AD;
-  --pst-green-500: #287977;
-  --pst-green-600: #246161;
-  --pst-green-700: #204F4F;
-  --pst-green-800: #1C3C3C;
-  --pst-green-900: #0D2427;
-  --pst-lilac-50: #f4eefb;
-  --pst-lilac-100: #DAD6FE;
-  --pst-lilac-200: #BCB2FD;
-  --pst-lilac-300: #9F8BFA;
-  --pst-lilac-400: #7F5CF6;
-  --pst-lilac-500: #6F3AED;
-  --pst-lilac-600: #6028D9;
-  --pst-lilac-700: #5021B6;
-  --pst-lilac-800: #431D95;
-  --pst-lilac-900: #1e0e39;
-  --pst-header-height: 2.5rem;
+pre {
+  white-space: break-spaces;
 }
 
-html {
-    --pst-font-family-base: 'Inter';
-    --pst-font-family-heading: 'Inter Tight', sans-serif;
+@media (min-width: 1200px) {
+  .container,
+  .container-lg,
+  .container-md,
+  .container-sm,
+  .container-xl {
+    max-width: 2560px !important;
+  }
 }
 
-/*******************************************************************************
-* write the color rules for each theme (light/dark)
-*/
-/* NOTE:
- * Mixins enable us to reuse the same definitions for the different modes
- * https://sass-lang.com/documentation/at-rules/mixin
- * something inserts a variable into a CSS selector or property name
- * https://sass-lang.com/documentation/interpolation
- */
-/* Defaults to light mode if data-theme is not set */
-html:not([data-theme]) {
-  --pst-color-primary: #287977;
-  --pst-color-primary-bg: #80D6D3;
-  --pst-color-secondary: #6F3AED;
-  --pst-color-secondary-bg: #DAD6FE;
-  --pst-color-accent: #c132af;
-  --pst-color-accent-bg: #f8dff5;
-  --pst-color-info: #276be9;
-  --pst-color-info-bg: #dce7fc;
-  --pst-color-warning: #f66a0a;
-  --pst-color-warning-bg: #f8e3d0;
-  --pst-color-success: #00843f;
-  --pst-color-success-bg: #d6ece1;
-  --pst-color-attention: var(--pst-color-warning);
-  --pst-color-attention-bg: var(--pst-color-warning-bg);
-  --pst-color-danger: #d72d47;
-  --pst-color-danger-bg: #f9e1e4;
-  --pst-color-text-base: #222832;
-  --pst-color-text-muted: #48566b;
-  --pst-color-heading-color: #ffffff;
-  --pst-color-shadow: rgba(0, 0, 0, 0.1);
-  --pst-color-border: #d1d5da;
-  --pst-color-border-muted: rgba(23, 23, 26, 0.2);
-  --pst-color-inline-code: #912583;
-  --pst-color-inline-code-links: #246161;
-  --pst-color-target: #f3cf95;
-  --pst-color-background: #ffffff;
-  --pst-color-on-background: #F4F9F8;
-  --pst-color-surface: #F4F9F8;
-  --pst-color-on-surface: #222832;
-}
-html:not([data-theme]) {
-  --pst-color-link: var(--pst-color-primary);
-  --pst-color-link-hover: var(--pst-color-secondary);
-}
-html:not([data-theme]) .only-dark,
-html:not([data-theme]) .only-dark ~ figcaption {
-  display: none !important;
+#my-component-root *,
+#headlessui-portal-root * {
+  z-index: 10000;
 }
 
-/* NOTE: @each {...} is like a for-loop
- * https://sass-lang.com/documentation/at-rules/control/each
- */
-html[data-theme=light] {
-  --pst-color-primary: #287977;
-  --pst-color-primary-bg: #80D6D3;
-  --pst-color-secondary: #6F3AED;
-  --pst-color-secondary-bg: #DAD6FE;
-  --pst-color-accent: #c132af;
-  --pst-color-accent-bg: #f8dff5;
-  --pst-color-info: #276be9;
-  --pst-color-info-bg: #dce7fc;
-  --pst-color-warning: #f66a0a;
-  --pst-color-warning-bg: #f8e3d0;
-  --pst-color-success: #00843f;
-  --pst-color-success-bg: #d6ece1;
-  --pst-color-attention: var(--pst-color-warning);
-  --pst-color-attention-bg: var(--pst-color-warning-bg);
-  --pst-color-danger: #d72d47;
-  --pst-color-danger-bg: #f9e1e4;
-  --pst-color-text-base: #222832;
-  --pst-color-text-muted: #48566b;
-  --pst-color-heading-color: #ffffff;
-  --pst-color-shadow: rgba(0, 0, 0, 0.1);
-  --pst-color-border: #d1d5da;
-  --pst-color-border-muted: rgba(23, 23, 26, 0.2);
-  --pst-color-inline-code: #912583;
-  --pst-color-inline-code-links: #246161;
-  --pst-color-target: #f3cf95;
-  --pst-color-background: #ffffff;
-  --pst-color-on-background: #F4F9F8;
-  --pst-color-surface: #F4F9F8;
-  --pst-color-on-surface: #222832;
-  color-scheme: light;
-}
-html[data-theme=light] {
-  --pst-color-link: var(--pst-color-primary);
-  --pst-color-link-hover: var(--pst-color-secondary);
-}
-html[data-theme=light] .only-dark,
-html[data-theme=light] .only-dark ~ figcaption {
-  display: none !important;
+table.longtable code {
+  white-space: normal;
 }
 
-html[data-theme=dark] {
-  --pst-color-primary: #4FB2AD;
-  --pst-color-primary-bg: #1C3C3C;
-  --pst-color-secondary: #7F5CF6;
-  --pst-color-secondary-bg: #431D95;
-  --pst-color-accent: #e47fd7;
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docs/api_reference/conf.py

@@ -15,8 +15,6 @@ from pathlib import Path
 
 import toml
 from docutils import nodes
-from docutils.parsers.rst.directives.admonitions import BaseAdmonition
-from docutils.statemachine import StringList
 from sphinx.util.docutils import SphinxDirective
 
 # If extensions (or modules to document with autodoc) are in another directory,
@@ -62,41 +60,26 @@ class ExampleLinksDirective(SphinxDirective):
             item_node.append(para_node)
             list_node.append(item_node)
         if list_node.children:
-            title_node = nodes.rubric()
+            title_node = nodes.title()
             title_node.append(nodes.Text(f"Examples using {class_or_func_name}"))
             return [title_node, list_node]
         return [list_node]
 
 
-class Beta(BaseAdmonition):
-    required_arguments = 0
-    node_class = nodes.admonition
-
-    def run(self):
-        self.content = self.content or StringList(
-            [
-                (
-                    "This feature is in beta. It is actively being worked on, so the "
-                    "API may change."
-                )
-            ]
-        )
-        self.arguments = self.arguments or ["Beta"]
-        return super().run()
-
-
 def setup(app):
     app.add_directive("example_links", ExampleLinksDirective)
-    app.add_directive("beta", Beta)
 
 
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, LangChain Inc"
-author = "LangChain, Inc"
+copyright = "2023, LangChain, Inc."
+author = "LangChain, Inc."
 
-html_favicon = "_static/img/brand/favicon.png"
+version = data["tool"]["poetry"]["version"]
+release = version
+
+html_title = project + " " + version
 html_last_updated_fmt = "%b %d, %Y"
 
 
@@ -112,13 +95,11 @@ extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
-    "IPython.sphinxext.ipython_console_highlighting",
-    "myst_parser",
-    "_extensions.gallery_directive",
-    "sphinx_design",
     "sphinx_copybutton",
+    "sphinx_panels",
+    "IPython.sphinxext.ipython_console_highlighting",
 ]
-source_suffix = [".rst", ".md"]
+source_suffix = [".rst"]
 
 # some autodoc pydantic options are repeated in the actual template.
 # potentially user error, but there may be bugs in the sphinx extension
@@ -150,84 +131,23 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-# The theme to use for HTML and HTML Help pages.
-html_theme = "pydata_sphinx_theme"
+html_theme = "scikit-learn-modern"
+html_theme_path = ["themes"]
 
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-html_theme_options = {
-    #     # -- General configuration ------------------------------------------------
-    "sidebar_includehidden": True,
-    "use_edit_page_button": False,
-    #     # "analytics": {
-    #     #     "plausible_analytics_domain": "scikit-learn.org",
-    #     #     "plausible_analytics_url": "https://views.scientific-python.org/js/script.js",
-    #     # },
-    #     # If "prev-next" is included in article_footer_items, then setting show_prev_next
-    #     # to True would repeat prev and next links. See
-    #     # https://github.com/pydata/pydata-sphinx-theme/blob/b731dc230bc26a3d1d1bb039c56c977a9b3d25d8/src/pydata_sphinx_theme/theme/pydata_sphinx_theme/layout.html#L118-L129
-    "show_prev_next": False,
-    "search_bar_text": "Search",
-    "navigation_with_keys": True,
-    "collapse_navigation": True,
-    "navigation_depth": 3,
-    "show_nav_level": 1,
-    "show_toc_level": 3,
-    "navbar_align": "left",
-    "header_links_before_dropdown": 5,
-    "header_dropdown_text": "Integrations",
-    "logo": {
-        "image_light": "_static/wordmark-api.svg",
-        "image_dark": "_static/wordmark-api-dark.svg",
-    },
-    "surface_warnings": True,
-    #     # -- Template placement in theme layouts ----------------------------------
-    "navbar_start": ["navbar-logo"],
-    #     # Note that the alignment of navbar_center is controlled by navbar_align
-    "navbar_center": ["navbar-nav"],
-    "navbar_end": ["langchain_docs", "theme-switcher", "navbar-icon-links"],
-    #     # navbar_persistent is persistent right (even when on mobiles)
-    "navbar_persistent": ["search-field"],
-    "article_header_start": ["breadcrumbs"],
-    "article_header_end": [],
-    "article_footer_items": [],
-    "content_footer_items": [],
-    #     # Use html_sidebars that map page patterns to list of sidebar templates
-    #     "primary_sidebar_end": [],
-    "footer_start": ["copyright"],
-    "footer_center": [],
-    "footer_end": [],
-    #     # When specified as a dictionary, the keys should follow glob-style patterns, as in
-    #     # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-exclude_patterns
-    #     # In particular, "**" specifies the default for all pages
-    #     # Use :html_theme.sidebar_secondary.remove: for file-wide removal
-    #     "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]},
-    #     "show_version_warning_banner": True,
-    #     "announcement": None,
-    "icon_links": [
-        {
-            # Label for this link
-            "name": "GitHub",
-            # URL where the link will redirect
-            "url": "https://github.com/langchain-ai/langchain",  # required
-            # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local")
-            "icon": "fa-brands fa-square-github",
-            # The type of image to be used (see below for details)
-            "type": "fontawesome",
-        },
-        {
-            "name": "X / Twitter",
-            "url": "https://twitter.com/langchainai",
-            "icon": "fab fa-twitter-square",
-        },
-    ],
-    "icon_links_label": "Quick Links",
-    "external_links": [
-        {"name": "Legacy reference", "url": "https://api.python.langchain.com/"},
-    ],
+# redirects dictionary maps from old links to new links
+html_additional_pages = {}
+redirects = {
+    "index": "langchain_api_reference",
 }
+for old_link in redirects:
+    html_additional_pages[old_link] = "redirects.html"
 
+partners_dir = Path(__file__).parent.parent.parent / "libs/partners"
+partners = [
+    (p.name, p.name.replace("-", "_") + "_api_reference")
+    for p in partners_dir.iterdir()
+]
+partners = sorted(partners)
 
 html_context = {
     "display_github": True,  # Integrate GitHub
@@ -235,6 +155,8 @@ html_context = {
     "github_repo": "langchain",  # Repo name
     "github_version": "master",  # Version
     "conf_py_path": "/docs/api_reference",  # Path in the checkout to the docs root
+    "redirects": redirects,
+    "partners": partners,
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,
@@ -244,7 +166,9 @@ html_static_path = ["_static"]
 
 # These paths are either relative to html_static_path
 # or fully qualified paths (e.g. https://...)
-html_css_files = ["css/custom.css"]
+html_css_files = [
+    "css/custom.css",
+]
 html_use_index = False
 
 myst_enable_extensions = ["colon_fence"]
@@ -254,12 +178,3 @@ autosummary_generate = True
 
 html_copy_source = False
 html_show_sourcelink = False
-
-# Set canonical URL from the Read the Docs Domain
-html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
-
-# Tell Jinja2 templates the build is running on Read the Docs
-if os.environ.get("READTHEDOCS", "") == "True":
-    html_context["READTHEDOCS"] = True
-
-master_doc = "index"

docs/api_reference/create_api_rst.py

@@ -10,21 +10,12 @@ from pathlib import Path
 from typing import Dict, List, Literal, Optional, Sequence, TypedDict, Union
 
 import toml
-import typing_extensions
-from langchain_core.runnables import Runnable, RunnableSerializable
 from pydantic import BaseModel
 
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
-ClassKind = Literal[
-    "TypedDict",
-    "Regular",
-    "Pydantic",
-    "enum",
-    "RunnablePydantic",
-    "RunnableNonPydantic",
-]
+ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
 class ClassInfo(TypedDict):
@@ -38,8 +29,6 @@ class ClassInfo(TypedDict):
     """The kind of the class."""
     is_public: bool
     """Whether the class is public or not."""
-    is_deprecated: bool
-    """Whether the class is deprecated."""
 
 
 class FunctionInfo(TypedDict):
@@ -51,8 +40,6 @@ class FunctionInfo(TypedDict):
     """The fully qualified name of the function."""
     is_public: bool
     """Whether the function is public or not."""
-    is_deprecated: bool
-    """Whether the function is deprecated."""
 
 
 class ModuleMembers(TypedDict):
@@ -82,36 +69,8 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
             continue
 
         if inspect.isclass(type_):
-            # The type of the class is used to select a template
-            # for the object when rendering the documentation.
-            # See `templates` directory for defined templates.
-            # This is a hacky solution to distinguish between different
-            # kinds of thing that we want to render.
-            if type(type_) is typing_extensions._TypedDictMeta:  # type: ignore
+            if type(type_) == typing._TypedDictMeta:  # type: ignore
                 kind: ClassKind = "TypedDict"
-            elif type(type_) is typing._TypedDictMeta:  # type: ignore
-                kind: ClassKind = "TypedDict"
-            elif (
-                issubclass(type_, Runnable)
-                and issubclass(type_, BaseModel)
-                and type_ is not Runnable
-            ):
-                # RunnableSerializable subclasses from Pydantic which
-                # for which we use autodoc_pydantic for rendering.
-                # We need to distinguish these from regular Pydantic
-                # classes so we can hide inherited Runnable methods
-                # and provide a link to the Runnable interface from
-                # the template.
-                kind = "RunnablePydantic"
-            elif (
-                issubclass(type_, Runnable)
-                and not issubclass(type_, BaseModel)
-                and type_ is not Runnable
-            ):
-                # These are not pydantic classes but are Runnable.
-                # We'll hide all the inherited methods from Runnable
-                # but use a regular class template to render.
-                kind = "RunnableNonPydantic"
             elif issubclass(type_, Enum):
                 kind = "enum"
             elif issubclass(type_, BaseModel):
@@ -125,7 +84,6 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
                     qualified_name=f"{namespace}.{name}",
                     kind=kind,
                     is_public=not name.startswith("_"),
-                    is_deprecated=".. deprecated::" in (type_.__doc__ or ""),
                 )
             )
         elif inspect.isfunction(type_):
@@ -134,7 +92,6 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
                     name=name,
                     qualified_name=f"{namespace}.{name}",
                     is_public=not name.startswith("_"),
-                    is_deprecated=".. deprecated::" in (type_.__doc__ or ""),
                 )
             )
         else:
@@ -239,7 +196,7 @@ def _construct_doc(
     package_namespace: str,
     members_by_namespace: Dict[str, ModuleMembers],
     package_version: str,
-) -> List[typing.Tuple[str, str]]:
+) -> str:
     """Construct the contents of the reference.rst file for the given package.
 
     Args:
@@ -251,62 +208,23 @@ def _construct_doc(
     Returns:
         The contents of the reference.rst file.
     """
-    docs = []
-    index_doc = f"""\
-:html_theme.sidebar_secondary.remove:
+    full_doc = f"""\
+=======================
+``{package_namespace}`` {package_version}
+=======================
 
-.. currentmodule:: {package_namespace}
-
-.. _{package_namespace}:
-
-======================================
-{package_namespace.replace('_', '-')}: {package_version}
-======================================
-
-.. automodule:: {package_namespace}
-    :no-members:
-    :no-inherited-members:
-
-.. toctree::
-    :hidden:
-    :maxdepth: 2
-    
-"""
-    index_autosummary = """
 """
     namespaces = sorted(members_by_namespace)
 
     for module in namespaces:
-        index_doc += f"    {module}\n"
-        module_doc = f"""\
-.. currentmodule:: {package_namespace}
-
-.. _{package_namespace}_{module}:
-"""
         _members = members_by_namespace[module]
-        classes = [
-            el
-            for el in _members["classes_"]
-            if el["is_public"] and not el["is_deprecated"]
-        ]
-        functions = [
-            el
-            for el in _members["functions"]
-            if el["is_public"] and not el["is_deprecated"]
-        ]
-        deprecated_classes = [
-            el for el in _members["classes_"] if el["is_public"] and el["is_deprecated"]
-        ]
-        deprecated_functions = [
-            el
-            for el in _members["functions"]
-            if el["is_public"] and el["is_deprecated"]
-        ]
+        classes = [el for el in _members["classes_"] if el["is_public"]]
+        functions = [el for el in _members["functions"] if el["is_public"]]
         if not (classes or functions):
             continue
-        section = f":mod:`{module}`"
+        section = f":mod:`{package_namespace}.{module}`"
         underline = "=" * (len(section) + 1)
-        module_doc += f"""
+        full_doc += f"""\
 {section}
 {underline}
 
@@ -314,26 +232,16 @@ def _construct_doc(
     :no-members:
     :no-inherited-members:
 
-"""
-
-        index_autosummary += f"""
-:ref:`{package_namespace}_{module}`
-{'^' * (len(package_namespace) + len(module) + 8)}
 """
 
         if classes:
-            module_doc += f"""\
-**Classes**
-
+            full_doc += f"""\
+Classes
+--------------
 .. currentmodule:: {package_namespace}
 
 .. autosummary::
     :toctree: {module}
-"""
-            index_autosummary += """
-**Classes**
-
-.. autosummary::
 """
 
             for class_ in sorted(classes, key=lambda c: c["qualified_name"]):
@@ -343,29 +251,22 @@ def _construct_doc(
                     template = "enum.rst"
                 elif class_["kind"] == "Pydantic":
                     template = "pydantic.rst"
-                elif class_["kind"] == "RunnablePydantic":
-                    template = "runnable_pydantic.rst"
-                elif class_["kind"] == "RunnableNonPydantic":
-                    template = "runnable_non_pydantic.rst"
                 else:
                     template = "class.rst"
 
-                module_doc += f"""\
+                full_doc += f"""\
     :template: {template}
     
     {class_["qualified_name"]}
     
-"""
-                index_autosummary += f"""
-    {class_['qualified_name']}
 """
 
         if functions:
             _functions = [f["qualified_name"] for f in functions]
             fstring = "\n    ".join(sorted(_functions))
-            module_doc += f"""\
-**Functions**
-
+            full_doc += f"""\
+Functions
+--------------
 .. currentmodule:: {package_namespace}
 
 .. autosummary::
@@ -375,80 +276,7 @@ def _construct_doc(
     {fstring}
 
 """
-
-            index_autosummary += f"""
-**Functions**
-
-.. autosummary::
-
-    {fstring}
-"""
-        if deprecated_classes:
-            module_doc += f"""\
-**Deprecated classes**
-
-.. currentmodule:: {package_namespace}
-
-.. autosummary::
-    :toctree: {module}
-"""
-
-            index_autosummary += """
-**Deprecated classes**
-
-.. autosummary::
-"""
-
-            for class_ in sorted(deprecated_classes, key=lambda c: c["qualified_name"]):
-                if class_["kind"] == "TypedDict":
-                    template = "typeddict.rst"
-                elif class_["kind"] == "enum":
-                    template = "enum.rst"
-                elif class_["kind"] == "Pydantic":
-                    template = "pydantic.rst"
-                elif class_["kind"] == "RunnablePydantic":
-                    template = "runnable_pydantic.rst"
-                elif class_["kind"] == "RunnableNonPydantic":
-                    template = "runnable_non_pydantic.rst"
-                else:
-                    template = "class.rst"
-
-                module_doc += f"""\
-    :template: {template}
-
-    {class_["qualified_name"]}
-
-"""
-                index_autosummary += f"""
-    {class_['qualified_name']}
-"""
-
-        if deprecated_functions:
-            _functions = [f["qualified_name"] for f in deprecated_functions]
-            fstring = "\n    ".join(sorted(_functions))
-            module_doc += f"""\
-**Deprecated functions**
-
-.. currentmodule:: {package_namespace}
-
-.. autosummary::
-    :toctree: {module}
-    :template: function.rst
-
-    {fstring}
-
-"""
-            index_autosummary += f"""
-**Deprecated functions**
-
-.. autosummary::
-
-    {fstring}
-
-"""
-        docs.append((f"{module}.rst", module_doc))
-    docs.append(("index.rst", index_doc + index_autosummary))
-    return docs
+    return full_doc
 
 
 def _build_rst_file(package_name: str = "langchain") -> None:
@@ -460,25 +288,16 @@ def _build_rst_file(package_name: str = "langchain") -> None:
     package_dir = _package_dir(package_name)
     package_members = _load_package_modules(package_dir)
     package_version = _get_package_version(package_dir)
-    output_dir = _out_file_path(package_name)
-    os.mkdir(output_dir)
-    rsts = _construct_doc(
-        _package_namespace(package_name), package_members, package_version
-    )
-    for name, rst in rsts:
-        with open(output_dir / name, "w") as f:
-            f.write(rst)
+    with open(_out_file_path(package_name), "w") as f:
+        f.write(
+            _doc_first_line(package_name)
+            + _construct_doc(
+                _package_namespace(package_name), package_members, package_version
+            )
+        )
 
 
 def _package_namespace(package_name: str) -> str:
-    """Returns the package name used.
-
-    Args:
-        package_name: Can be either "langchain" or "core" or "experimental".
-
-    Returns:
-        modified package_name: Can be either "langchain" or "langchain_{package_name}"
-    """
     return (
         package_name
         if package_name == "langchain"
@@ -525,119 +344,12 @@ def _get_package_version(package_dir: Path) -> str:
 
 def _out_file_path(package_name: str) -> Path:
     """Return the path to the file containing the documentation."""
-    return HERE / f"{package_name.replace('-', '_')}"
+    return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
-def _build_index(dirs: List[str]) -> None:
-    custom_names = {
-        "airbyte": "Airbyte",
-        "aws": "AWS",
-        "ai21": "AI21",
-    }
-    ordered = ["core", "langchain", "text-splitters", "community", "experimental"]
-    main_ = [dir_ for dir_ in ordered if dir_ in dirs]
-    integrations = sorted(dir_ for dir_ in dirs if dir_ not in main_)
-    doc = """# LangChain Python API Reference
-
-Welcome to the LangChain Python API reference. This is a reference for all 
-`langchain-x` packages. 
-
-For user guides see [https://python.langchain.com](https://python.langchain.com).
-
-For the legacy API reference hosted on ReadTheDocs see [https://api.python.langchain.com/](https://api.python.langchain.com/).
-"""
-
-    if main_:
-        main_headers = [
-            " ".join(custom_names.get(x, x.title()) for x in dir_.split("-"))
-            for dir_ in main_
-        ]
-        main_tree = "\n".join(
-            f"{header_name}<{dir_.replace('-', '_')}/index>"
-            for header_name, dir_ in zip(main_headers, main_)
-        )
-        main_grid = "\n".join(
-            f'- header: "**{header_name}**"\n  content: "{_package_namespace(dir_).replace("_", "-")}: {_get_package_version(_package_dir(dir_))}"\n  link: {dir_.replace("-", "_")}/index.html'
-            for header_name, dir_ in zip(main_headers, main_)
-        )
-        doc += f"""## Base packages
-
-```{{gallery-grid}}
-:grid-columns: "1 2 2 3"
-
-{main_grid}
-```
-
-```{{toctree}}
-:maxdepth: 2
-:hidden:
-:caption: Base packages
-
-{main_tree}
-```
-"""
-    if integrations:
-        integration_headers = [
-            " ".join(
-                custom_names.get(x, x.title().replace("ai", "AI").replace("db", "DB"))
-                for x in dir_.split("-")
-            )
-            for dir_ in integrations
-        ]
-        integration_tree = "\n".join(
-            f"{header_name}<{dir_.replace('-', '_')}/index>"
-            for header_name, dir_ in zip(integration_headers, integrations)
-        )
-
-        integration_grid = ""
-        integrations_to_show = [
-            "openai",
-            "anthropic",
-            "google-vertexai",
-            "aws",
-            "huggingface",
-            "mistralai",
-        ]
-        for header_name, dir_ in sorted(
-            zip(integration_headers, integrations),
-            key=lambda h_d: integrations_to_show.index(h_d[1])
-            if h_d[1] in integrations_to_show
-            else len(integrations_to_show),
-        )[: len(integrations_to_show)]:
-            integration_grid += f'\n- header: "**{header_name}**"\n  content: {_package_namespace(dir_).replace("_", "-")} {_get_package_version(_package_dir(dir_))}\n  link: {dir_.replace("-", "_")}/index.html'
-        doc += f"""## Integrations
-
-```{{gallery-grid}}
-:grid-columns: "1 2 2 3"
-
-{integration_grid}
-```
-
-See the full list of integrations in the Section Navigation.
-
-```{{toctree}}
-:maxdepth: 2
-:hidden:
-:caption: Integrations
-
-{integration_tree}
-```
-"""
-    with open(HERE / "reference.md", "w") as f:
-        f.write(doc)
-
-    dummy_index = """\
-# API reference
-
-```{toctree}
-:maxdepth: 3
-:hidden:
-
-Reference<reference>
-```
-"""
-    with open(HERE / "index.md", "w") as f:
-        f.write(dummy_index)
+def _doc_first_line(package_name: str) -> str:
+    """Return the path to the file containing the documentation."""
+    return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
 
 
 def main(dirs: Optional[list] = None) -> None:
@@ -665,8 +377,6 @@ def main(dirs: Optional[list] = None) -> None:
         else:
             print("Building package:", dir_)
             _build_rst_file(package_name=dir_)
-
-    _build_index(dirs)
     print("API reference files built.")
 
 

docs/api_reference/index.rst

@@ -0,0 +1,8 @@
+=============
+LangChain API
+=============
+
+.. toctree::
+    :maxdepth: 2
+
+    api_reference.rst

docs/api_reference/requirements.txt

@@ -1,11 +1,17 @@
-autodoc_pydantic>=1,<2
-sphinx<=7
-myst-parser>=3
-sphinx-autobuild>=2024
-pydata-sphinx-theme>=0.15
-toml>=0.10.2
-myst-nb>=1.1.1
-pyyaml
-sphinx-design
-sphinx-copybutton
-beautifulsoup4
+-e libs/experimental
+-e libs/langchain
+-e libs/core
+-e libs/community
+pydantic<2
+autodoc_pydantic==1.8.0
+myst_parser
+nbsphinx==0.8.9
+sphinx>=5
+sphinx-autobuild==2021.3.14
+sphinx_rtd_theme==1.0.0
+sphinx-typlog-theme==0.8.0
+sphinx-panels
+toml
+myst_nb
+sphinx_copybutton
+pydata-sphinx-theme==0.13.1

docs/api_reference/scripts/custom_formatter.py

@@ -1,41 +0,0 @@
-import sys
-from glob import glob
-from pathlib import Path
-
-from bs4 import BeautifulSoup
-
-CUR_DIR = Path(__file__).parents[1]
-
-
-def process_toc_h3_elements(html_content: str) -> str:
-    """Update Class.method() TOC headers to just method()."""
-    # Create a BeautifulSoup object
-    soup = BeautifulSoup(html_content, "html.parser")
-
-    # Find all <li> elements with class "toc-h3"
-    toc_h3_elements = soup.find_all("li", class_="toc-h3")
-
-    # Process each element
-    for element in toc_h3_elements:
-        element = element.a.code.span
-        # Get the text content of the element
-        content = element.get_text()
-
-        # Apply the regex substitution
-        modified_content = content.split(".")[-1]
-
-        # Update the element's content
-        element.string = modified_content
-
-    # Return the modified HTML
-    return str(soup)
-
-
-if __name__ == "__main__":
-    dir = sys.argv[1]
-    for fn in glob(str(f"{dir.rstrip('/')}/**/*.html"), recursive=True):
-        with open(fn, "r") as f:
-            html = f.read()
-        processed_html = process_toc_h3_elements(html)
-        with open(fn, "w") as f:
-            f.write(processed_html)

docs/api_reference/templates/class.rst

@@ -1,4 +1,4 @@
-{{ objname }}
+:mod:`{{module}}`.{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}
@@ -11,7 +11,7 @@
 
    .. autosummary::
    {% for item in attributes %}
-      ~{{ item }}
+      ~{{ name }}.{{ item }}
    {%- endfor %}
    {% endif %}
    {% endblock %}
@@ -22,15 +22,15 @@
 
    .. autosummary::
    {% for item in methods %}
-      ~{{ item }}
+      ~{{ name }}.{{ item }}
    {%- endfor %}
 
    {% for item in methods %}
-   .. automethod:: {{ item }}
+   .. automethod:: {{ name }}.{{ item }}
    {%- endfor %}
 
    {% endif %}
    {% endblock %}
 
 
-.. example_links:: {{ objname }}
+.. example_links:: {{ objname }}

docs/api_reference/templates/enum.rst

@@ -1,4 +1,4 @@
-{{ objname }}
+:mod:`{{module}}`.{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/templates/function.rst

@@ -1,4 +1,4 @@
-{{ objname }}
+:mod:`{{module}}`.{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/templates/langchain_docs.html

@@ -1,12 +0,0 @@
-<!-- This will display a link to LangChain docs -->
-<head>
-    <style>
-        .text-link {
-            text-decoration: none; /* Remove underline */
-            color: inherit;        /* Inherit color from parent element */
-        }
-    </style>
-</head>
-<body>
-<a href="https://python.langchain.com/" class='text-link'>Docs</a>
-</body>

docs/api_reference/templates/pydantic.rst

@@ -1,4 +1,4 @@
-{{ objname }}
+:mod:`{{module}}`.{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}
@@ -15,8 +15,6 @@
     :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
-
 
     {% block attributes %}
     {% endblock %}

docs/api_reference/templates/runnable_non_pydantic.rst

@@ -1,40 +0,0 @@
-{{ objname }}
-{{ underline }}==============
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-.. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
-
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
-
-   {% block attributes %}
-   {% if attributes %}
-   .. rubric:: {{ _('Attributes') }}
-
-   .. autosummary::
-   {% for item in attributes %}
-      ~{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-   {% block methods %}
-   {% if methods %}
-   .. rubric:: {{ _('Methods') }}
-
-   .. autosummary::
-   {% for item in methods %}
-      ~{{ item }}
-   {%- endfor %}
-
-   {% for item in methods %}
-   .. automethod:: {{ item }}
-   {%- endfor %}
-
-   {% endif %}
-   {% endblock %}
-
-
-.. example_links:: {{ objname }}

docs/api_reference/templates/runnable_pydantic.rst

@@ -1,24 +0,0 @@
-{{ objname }}
-{{ underline }}==============
-
-.. currentmodule:: {{ module }}
-
-.. autopydantic_model:: {{ objname }}
-    :model-show-json: False
-    :model-show-config-summary: False
-    :model-show-validator-members: False
-    :model-show-field-summary: False
-    :field-signature-prefix: param
-    :members:
-    :undoc-members:
-    :inherited-members:
-    :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, 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>`. 🏃
-
-        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
-
-.. example_links:: {{ objname }}

docs/api_reference/templates/typeddict.rst

@@ -1,4 +1,4 @@
-{{ objname }}
+:mod:`{{module}}`.{{objname}}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/themes/COPYRIGHT.txt

@@ -0,0 +1,27 @@
+Copyright (c) 2007-2023 The scikit-learn developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docs/api_reference/themes/scikit-learn-modern/javascript.html

@@ -0,0 +1,67 @@
+<script>
+$(document).ready(function() {
+    /* Add a [>>>] button on the top-right corner of code samples to hide
+     * the >>> and ... prompts and the output and thus make the code
+     * copyable. */
+    var div = $('.highlight-python .highlight,' +
+                '.highlight-python3 .highlight,' +
+                '.highlight-pycon .highlight,' +
+		'.highlight-default .highlight')
+    var pre = div.find('pre');
+
+    // get the styles from the current theme
+    pre.parent().parent().css('position', 'relative');
+    var hide_text = 'Hide prompts and outputs';
+    var show_text = 'Show prompts and outputs';
+
+    // create and add the button to all the code blocks that contain >>>
+    div.each(function(index) {
+        var jthis = $(this);
+        if (jthis.find('.gp').length > 0) {
+            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
+            button.attr('title', hide_text);
+            button.data('hidden', 'false');
+            jthis.prepend(button);
+        }
+        // tracebacks (.gt) contain bare text elements that need to be
+        // wrapped in a span to work with .nextUntil() (see later)
+        jthis.find('pre:has(.gt)').contents().filter(function() {
+            return ((this.nodeType == 3) && (this.data.trim().length > 0));
+        }).wrap('<span>');
+    });
+
+    // define the behavior of the button when it's clicked
+    $('.copybutton').click(function(e){
+        e.preventDefault();
+        var button = $(this);
+        if (button.data('hidden') === 'false') {
+            // hide the code output
+            button.parent().find('.go, .gp, .gt').hide();
+            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
+            button.css('text-decoration', 'line-through');
+            button.attr('title', show_text);
+            button.data('hidden', 'true');
+        } else {
+            // show the code output
+            button.parent().find('.go, .gp, .gt').show();
+            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
+            button.css('text-decoration', 'none');
+            button.attr('title', hide_text);
+            button.data('hidden', 'false');
+        }
+    });
+
+	/*** Add permalink buttons next to glossary terms ***/
+	$('dl.glossary > dt[id]').append(function() {
+		return ('<a class="headerlink" href="#' +
+			    this.getAttribute('id') +
+			    '" title="Permalink to this term">¶</a>');
+	});
+});
+
+</script>
+{%- if pagename != 'index' and pagename != 'documentation' %}
+    {% if theme_mathjax_path %}
+<script id="MathJax-script" async src="{{ theme_mathjax_path }}"></script>
+    {% endif %}
+{%- endif %}

docs/api_reference/themes/scikit-learn-modern/layout.html

@@ -0,0 +1,135 @@
+{# TEMPLATE VAR SETTINGS #}
+{%- set url_root = pathto('', 1) %}
+{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
+{%- if not embedded and docstitle %}
+  {%- set titlesuffix = " — "|safe + docstitle|e %}
+{%- else %}
+  {%- set titlesuffix = "" %}
+{%- endif %}
+{%- set lang_attr = 'en' %}
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" > <!--<![endif]-->
+<head>
+  <meta charset="utf-8">
+  {{ metatags }}
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+  {% block htmltitle %}
+  <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
+  {% endblock %}
+  <link rel="canonical" href="https://api.python.langchain.com/en/latest/{{pagename}}.html" />
+
+  {% if favicon_url %}
+  <link rel="shortcut icon" href="{{ favicon_url|e }}"/>
+  {% endif %}
+
+  <link rel="stylesheet" href="{{ pathto('_static/css/vendor/bootstrap.min.css', 1) }}" type="text/css" />
+  {%- for css in css_files %}
+    {%- if css|attr("rel") %}
+  <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
+    {%- else %}
+  <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
+    {%- endif %}
+  {%- endfor %}
+  <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
+<script id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
+<script src="{{ pathto('_static/jquery.js', 1) }}"></script>
+{%- block extrahead %} {% endblock %}
+</head>
+<body>
+{% include "nav.html" %}
+{%- block content %}
+<div class="d-flex" id="sk-doc-wrapper">
+    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
+    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
+    <div id="sk-sidebar-wrapper" class="border-right">
+      <div class="sk-sidebar-toc-wrapper">
+        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
+          {%- if prev %}
+            <a href="{{ prev.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ prev.title|striptags }}">Prev</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Prev</a>
+          {%- endif %}
+          {%- if parents -%}
+            <a href="{{ parents[-1].link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ parents[-1].title|striptags }}">Up</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink disabled py-1">Up</a>
+          {%- endif %}
+          {%- if next %}
+            <a href="{{ next.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ next.title|striptags }}">Next</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Next</a>
+          {%- endif %}
+        </div>
+            {%- if meta and meta['parenttoc']|tobool %}
+            <div class="sk-sidebar-toc">
+            {% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %}
+              <ul>
+              {% for main_nav_item in nav %}
+              {% if main_nav_item.active %}
+              <li>
+                <a href="{{ main_nav_item.url }}" class="sk-toc-active">{{ main_nav_item.title }}</a>
+              </li>
+              <ul>
+              {% for nav_item in main_nav_item.children %}
+                <li>
+                  <a href="{{ nav_item.url }}" class="{% if nav_item.active %}sk-toc-active{% endif %}">{{ nav_item.title }}</a>
+                  {% if nav_item.children %}
+                  <ul>
+                    {% for inner_child in nav_item.children %}
+                      <li class="sk-toctree-l3">
+                        <a href="{{ inner_child.url }}">{{ inner_child.title }}</a>
+                      </li>
+                    {% endfor %}
+                  </ul>
+                  {% endif %}
+                </li>
+              {% endfor %}
+              </ul>
+              {% endif %}
+              {% endfor %}
+              </ul>
+            </div>
+            {%- elif meta and meta['globalsidebartoc']|tobool %}
+            <div class="sk-sidebar-toc sk-sidebar-global-toc">
+              {{ toctree(maxdepth=2, titles_only=True) }}
+            </div>
+            {%- else %}
+            <div class="sk-sidebar-toc">
+              {{ toc }}
+            </div>
+            {%- endif %}
+      </div>
+    </div>
+    <div id="sk-page-content-wrapper">
+      <div class="sk-page-content container-fluid body px-md-3" role="main">
+        {% block body %}{% endblock %}
+      </div>
+    <div class="container">
+      <footer class="sk-content-footer">
+        {%- if pagename != 'index' %}
+        {%- if show_copyright %}
+          {%- if hasdoc('copyright') %}
+            {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
+          {%- else %}
+            {% trans copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
+          {%- endif %}
+        {%- endif %}
+        {%- if last_updated %}
+          {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
+        {%- endif %}
+        {%- if show_source and has_source and sourcename %}
+          <a href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">{{ _('Show this page source') }}</a>
+        {%- endif %}
+        {%- endif %}
+      </footer>
+    </div>
+  </div>
+</div>
+{%- endblock %}
+<script src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
+{% include "javascript.html" %}
+</body>
+</html>

docs/api_reference/themes/scikit-learn-modern/nav.html

@@ -0,0 +1,78 @@
+{%- if pagename != 'index' and pagename != 'documentation' %}
+  {%- set nav_bar_class = "sk-docs-navbar" %}
+  {%- set top_container_cls = "sk-docs-container" %}
+{%- else %}
+  {%- set nav_bar_class = "sk-landing-navbar" %}
+  {%- set top_container_cls = "sk-landing-container" %}
+{%- endif %}
+
+<nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
+  <div class="container-fluid {{ top_container_cls }} px-0">
+    {%- if logo_url %}
+      <a class="navbar-brand py-0" href="{{ pathto('index') }}">
+        <img
+          class="sk-brand-img"
+          src="{{ logo_url|e }}"
+          alt="logo"/>
+      </a>
+    {%- endif %}
+    <button
+      id="sk-navbar-toggler"
+      class="navbar-toggler"
+      type="button"
+      data-toggle="collapse"
+      data-target="#navbarSupportedContent"
+      aria-controls="navbarSupportedContent"
+      aria-expanded="false"
+      aria-label="Toggle navigation"
+    >
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
+      <ul class="navbar-nav mr-auto">
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('langchain_api_reference') }}">LangChain</a>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('core_api_reference') }}">Core</a>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('community_api_reference') }}">Community</a>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('experimental_api_reference') }}">Experimental</a>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('text_splitters_api_reference') }}">Text splitters</a>
+        </li>
+        {%- for title, pathname in partners %}
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ pathto(pathname) }}">{{ title }}</a>
+        </li>
+        {%- endfor %}
+        <li class="nav-item dropdown nav-more-item-dropdown">
+          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Partner libs</a>
+          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
+            {%- for title, pathname in partners %}
+              <a class="sk-nav-dropdown-item dropdown-item" href="{{ pathto(pathname) }}">{{ title }}</a>
+            {%- endfor %}
+          </div>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Docs</a>
+        </li>
+      </ul>
+      {%- if pagename != "search"%}
+      <div id="searchbox" role="search">
+          <div class="searchformwrapper">
+          <form class="search" action="{{ pathto('search') }}" method="get">
+            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
+            <input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
+          </form>
+          </div>
+      </div>
+      {%- endif %}
+    </div>
+  </div>
+</nav>

docs/api_reference/themes/scikit-learn-modern/search.html

@@ -0,0 +1,16 @@
+{%- extends "basic/search.html" %}
+{% block extrahead %}
+  <script type="text/javascript" src="{{ pathto('_static/underscore.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('searchindex.js', 1) }}" defer></script>
+  <script type="text/javascript" src="{{ pathto('_static/doctools.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('_static/language_data.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script>
+  <script type="text/javascript">
+    $(document).ready(function() {
+      if (!Search.out) {
+        Search.init();
+      }
+    });
+  </script>
+{% endblock %}

docs/api_reference/themes/scikit-learn-modern/theme.conf

@@ -0,0 +1,8 @@
+[theme]
+inherit = basic
+pygments_style = default
+stylesheet = css/theme.css
+
+[options]
+link_to_live_contributing_page = false
+mathjax_path =

docs/docs/additional_resources/arxiv_references.mdx

@@ -4,90 +4,50 @@ LangChain implements the latest research in the field of Natural Language Proces
 This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
  Templates, and Cookbooks.
 
-From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. 
-
-`arXiv` papers with references to:
- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
+From the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. 
+Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header).
 
 ## Summary
 
 | 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/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/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/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/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/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/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/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/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/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/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/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)
+| `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)
+| `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 | `Cookbook:` [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
+| `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 | `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 | `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)
+| `2307.09288v2` [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)
+| `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)
+| `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_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.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)
+| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler,  et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
+| `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...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...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_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.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 | `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)
+| `2212.07425v3` [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...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
+| `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)
+| `2205.12654v1` [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 | `API:` [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
+| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022-02-01 | `API:` [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), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `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)
+| `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_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), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
 
-## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
-
-- **Authors:** Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al.
-- **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2)  **Published Date:** 2024-03-21
-- **LangChain:**
-
-   - **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
-as a promising approach to enhancing response accuracy in several tasks, such
-as Question-Answering (QA). However, even though there are various approaches
-dealing with queries of different complexities, they either handle simple
-queries with unnecessary computational overhead or fail to adequately address
-complex multi-step queries; yet, not all user requests fall into only one of
-the simple or complex categories. In this work, we propose a novel adaptive QA
-framework, that can dynamically select the most suitable strategy for
-(retrieval-augmented) LLMs from the simplest to the most sophisticated ones
-based on the query complexity. Also, this selection process is operationalized
-with a classifier, which is a smaller LM trained to predict the complexity
-level of incoming queries with automatically collected labels, obtained from
-actual predicted outcomes of models and inherent inductive biases in datasets.
-This approach offers a balanced strategy, seamlessly adapting between the
-iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval
-methods, in response to a range of query complexities. We validate our model on
-a set of open-domain QA datasets, covering multiple query complexities, and
-show that ours enhances the overall efficiency and accuracy of QA systems,
-compared to relevant baselines including the adaptive retrieval approaches.
-Code is available at: https://github.com/starsuzi/Adaptive-RAG.
-                
 ## Self-Discover: Large Language Models Self-Compose Reasoning Structures
 
+- **arXiv id:** 2402.03620v1
+- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
 - **Authors:** Pei Zhou, Jay Pujara, Xiang Ren,  et al.
-- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
+- **Published Date:** 2024-02-06
+- **URL:** http://arxiv.org/abs/2402.03620v1
 - **LangChain:**
 
    - **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
@@ -107,33 +67,13 @@ the self-discovered reasoning structures are universally applicable across
 model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share
 commonalities with human reasoning patterns.
                 
-## RAG-Fusion: a New Take on Retrieval-Augmented Generation
-
-- **Authors:** Zackary Rackauckas
-- **arXiv id:** [2402.03367v2](http://arxiv.org/abs/2402.03367v2)  **Published Date:** 2024-01-31
-- **LangChain:**
-
-   - **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
-with retrieval-augmented generation (RAG) chatbots, but in this study, I
-evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion
-combines RAG and reciprocal rank fusion (RRF) by generating multiple queries,
-reranking them with reciprocal scores and fusing the documents and scores.
-Through manually evaluating answers on accuracy, relevance, and
-comprehensiveness, I found that RAG-Fusion was able to provide accurate and
-comprehensive answers due to the generated queries contextualizing the original
-query from various perspectives. However, some answers strayed off topic when
-the generated queries' relevance to the original query is insufficient. This
-research marks significant progress in artificial intelligence (AI) and natural
-language processing (NLP) applications and demonstrates transformations in a
-global and multi-industry context.
-                
 ## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 
+- **arXiv id:** 2401.18059v1
+- **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 - **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al.
-- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
+- **Published Date:** 2024-01-31
+- **URL:** http://arxiv.org/abs/2401.18059v1
 - **LangChain:**
 
    - **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
@@ -155,11 +95,13 @@ benchmark by 20% in absolute accuracy.
                 
 ## Corrective Retrieval Augmented Generation
 
+- **arXiv id:** 2401.15884v2
+- **Title:** Corrective Retrieval Augmented Generation
 - **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al.
-- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
+- **Published Date:** 2024-01-29
+- **URL:** http://arxiv.org/abs/2401.15884v2
 - **LangChain:**
 
-   - **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
@@ -181,36 +123,13 @@ RAG-based approaches. Experiments on four datasets covering short- and
 long-form generation tasks show that CRAG can significantly improve the
 performance of RAG-based approaches.
                 
-## Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
-
-- **Authors:** Tal Ridnik, Dedy Kredo, Itamar Friedman
-- **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1)  **Published Date:** 2024-01-16
-- **LangChain:**
-
-   - **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
-paths and edge cases, paying attention to numerous small details in the problem
-spec, and addressing other code-specific issues and requirements. Hence, many
-of the optimizations and tricks that have been successful in natural language
-generation may not be effective for code tasks. In this work, we propose a new
-approach to code generation by LLMs, which we call AlphaCodium - a test-based,
-multi-stage, code-oriented iterative flow, that improves the performances of
-LLMs on code problems. We tested AlphaCodium on a challenging code generation
-dataset called CodeContests, which includes competitive programming problems
-from platforms such as Codeforces. The proposed flow consistently and
-significantly improves results. On the validation set, for example, GPT-4
-accuracy (pass@5) increased from 19% with a single well-designed direct prompt
-to 44% with the AlphaCodium flow. Many of the principles and best practices
-acquired in this work, we believe, are broadly applicable to general code
-generation tasks. Full implementation is available at:
-https://github.com/Codium-ai/AlphaCodium
-                
 ## Mixtral of Experts
 
+- **arXiv id:** 2401.04088v1
+- **Title:** Mixtral of Experts
 - **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al.
-- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
+- **Published Date:** 2024-01-08
+- **URL:** http://arxiv.org/abs/2401.04088v1
 - **LangChain:**
 
    - **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
@@ -232,8 +151,11 @@ the base and instruct models are released under the Apache 2.0 license.
                 
 ## Dense X Retrieval: What Retrieval Granularity Should We Use?
 
+- **arXiv id:** 2312.06648v2
+- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
 - **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
-- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
+- **Published Date:** 2023-12-11
+- **URL:** http://arxiv.org/abs/2312.06648v2
 - **LangChain:**
 
    - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
@@ -258,8 +180,11 @@ information.
                 
 ## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 
+- **arXiv id:** 2311.09210v1
+- **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 - **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
-- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
+- **Published Date:** 2023-11-15
+- **URL:** http://arxiv.org/abs/2311.09210v1
 - **LangChain:**
 
    - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
@@ -289,11 +214,13 @@ outside the pre-training knowledge scope.
                 
 ## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 
+- **arXiv id:** 2310.11511v1
+- **Title:** Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 - **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang,  et al.
-- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
+- **Published Date:** 2023-10-17
+- **URL:** http://arxiv.org/abs/2310.11511v1
 - **LangChain:**
 
-   - **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
@@ -320,11 +247,13 @@ to these models.
                 
 ## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 
+- **arXiv id:** 2310.06117v2
+- **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 - **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
-- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
+- **Published Date:** 2023-10-09
+- **URL:** http://arxiv.org/abs/2310.06117v2
 - **LangChain:**
 
-   - **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)
 
@@ -339,31 +268,13 @@ including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back
 Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7%
 and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
                 
-## Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
-
-- **Authors:** Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al.
-- **arXiv id:** [2307.15337v3](http://arxiv.org/abs/2307.15337v3)  **Published Date:** 2023-07-28
-- **LangChain:**
-
-   - **Template:** [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
-
-**Abstract:** This work aims at decreasing the end-to-end generation latency of large
-language models (LLMs). One of the major causes of the high generation latency
-is the sequential decoding approach adopted by almost all state-of-the-art
-LLMs. In this work, motivated by the thinking and writing process of humans, we
-propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the
-skeleton of the answer, and then conducts parallel API calls or batched
-decoding to complete the contents of each skeleton point in parallel. Not only
-does SoT provide considerable speed-ups across 12 LLMs, but it can also
-potentially improve the answer quality on several question categories. SoT is
-an initial attempt at data-centric optimization for inference efficiency, and
-showcases the potential of eliciting high-quality answers by explicitly
-planning the answer structure in language.
-                
 ## Llama 2: Open Foundation and Fine-Tuned Chat Models
 
+- **arXiv id:** 2307.09288v2
+- **Title:** Llama 2: Open Foundation and Fine-Tuned Chat Models
 - **Authors:** Hugo Touvron, Louis Martin, Kevin Stone,  et al.
-- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
+- **Published Date:** 2023-07-18
+- **URL:** http://arxiv.org/abs/2307.09288v2
 - **LangChain:**
 
    - **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
@@ -378,32 +289,13 @@ detailed description of our approach to fine-tuning and safety improvements of
 Llama 2-Chat in order to enable the community to build on our work and
 contribute to the responsible development of LLMs.
                 
-## Lost in the Middle: How Language Models Use Long Contexts
-
-- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt,  et al.
-- **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/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
-the performance of language models on two tasks that require identifying
-relevant information in their input contexts: multi-document question answering
-and key-value retrieval. We find that performance can degrade significantly
-when changing the position of relevant information, indicating that current
-language models do not robustly make use of information in long input contexts.
-In particular, we observe that performance is often highest when relevant
-information occurs at the beginning or end of the input context, and
-significantly degrades when models must access relevant information in the
-middle of long contexts, even for explicitly long-context models. Our analysis
-provides a better understanding of how language models use their input context
-and provides new evaluation protocols for future long-context language models.
-                
 ## Query Rewriting for Retrieval-Augmented Large Language Models
 
+- **arXiv id:** 2305.14283v3
+- **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
 - **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
-- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
+- **Published Date:** 2023-05-23
+- **URL:** http://arxiv.org/abs/2305.14283v3
 - **LangChain:**
 
    - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
@@ -429,8 +321,11 @@ for retrieval-augmented LLM.
                 
 ## Large Language Model Guided Tree-of-Thought
 
+- **arXiv id:** 2305.08291v1
+- **Title:** Large Language Model Guided Tree-of-Thought
 - **Authors:** Jieyi Long
-- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
+- **Published Date:** 2023-05-15
+- **URL:** http://arxiv.org/abs/2305.08291v1
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
@@ -456,8 +351,11 @@ implementation of the ToT-based Sudoku solver is available on GitHub:
                 
 ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 
+- **arXiv id:** 2305.04091v3
+- **Title:** Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 - **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan,  et al.
-- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
+- **Published Date:** 2023-05-06
+- **URL:** http://arxiv.org/abs/2305.04091v3
 - **LangChain:**
 
    - **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
@@ -484,37 +382,16 @@ Prompting, and has comparable performance with 8-shot CoT prompting on the math
 reasoning problem. The code can be found at
 https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
                 
-## Zero-Shot Listwise Document Reranking with a Large Language Model
-
-- **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al.
-- **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/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
-have shown success in multi-stage text ranking tasks, but they require large
-amounts of relevance judgments as training data. In this work, we propose
-Listwise Reranker with a Large Language Model (LRL), which achieves strong
-reranking effectiveness without using any task-specific training data.
-Different from the existing pointwise ranking methods, where documents are
-scored independently and ranked according to the scores, LRL directly generates
-a reordered list of document identifiers given the candidate documents.
-Experiments on three TREC web search datasets demonstrate that LRL not only
-outperforms zero-shot pointwise methods when reranking first-stage retrieval
-results, but can also act as a final-stage reranker to improve the top-ranked
-results of a pointwise method for improved efficiency. Additionally, we apply
-our approach to subsets of MIRACL, a recent multilingual retrieval dataset,
-with results showing its potential to generalize across different languages.
-                
 ## Visual Instruction Tuning
 
+- **arXiv id:** 2304.08485v2
+- **Title:** Visual Instruction Tuning
 - **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu,  et al.
-- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
+- **Published Date:** 2023-04-17
+- **URL:** http://arxiv.org/abs/2304.08485v2
 - **LangChain:**
 
-   - **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)
+   - **Cookbook:** [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
 
 **Abstract:** Instruction tuning large language models (LLMs) using machine-generated
 instruction-following data has improved zero-shot capabilities on new tasks,
@@ -534,8 +411,11 @@ publicly available.
                 
 ## Generative Agents: Interactive Simulacra of Human Behavior
 
+- **arXiv id:** 2304.03442v2
+- **Title:** Generative Agents: Interactive Simulacra of Human Behavior
 - **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al.
-- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
+- **Published Date:** 2023-04-07
+- **URL:** http://arxiv.org/abs/2304.03442v2
 - **LangChain:**
 
    - **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)
@@ -567,8 +447,11 @@ interaction patterns for enabling believable simulations of human behavior.
                 
 ## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 
+- **arXiv id:** 2303.17760v2
+- **Title:** CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 - **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al.
-- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
+- **Published Date:** 2023-03-31
+- **URL:** http://arxiv.org/abs/2303.17760v2
 - **LangChain:**
 
    - **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
@@ -594,8 +477,11 @@ agents and beyond: https://github.com/camel-ai/camel.
                 
 ## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 
+- **arXiv id:** 2303.17580v4
+- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 - **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
-- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
+- **Published Date:** 2023-03-30
+- **URL:** http://arxiv.org/abs/2303.17580v4
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
@@ -621,13 +507,40 @@ modalities and domains and achieve impressive results in language, vision,
 speech, and other challenging tasks, which paves a new way towards the
 realization of artificial general intelligence.
                 
-## A Watermark for Large Language Models
+## GPT-4 Technical Report
 
-- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
-- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
+- **arXiv id:** 2303.08774v6
+- **Title:** GPT-4 Technical Report
+- **Authors:** OpenAI, Josh Achiam, Steven Adler,  et al.
+- **Published Date:** 2023-03-15
+- **URL:** http://arxiv.org/abs/2303.08774v6
 - **LangChain:**
 
-   - **API Reference:** [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)
+   - **Documentation:** [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
+
+**Abstract:** We report the development of GPT-4, a large-scale, multimodal model which can
+accept image and text inputs and produce text outputs. While less capable than
+humans in many real-world scenarios, GPT-4 exhibits human-level performance on
+various professional and academic benchmarks, including passing a simulated bar
+exam with a score around the top 10% of test takers. GPT-4 is a
+Transformer-based model pre-trained to predict the next token in a document.
+The post-training alignment process results in improved performance on measures
+of factuality and adherence to desired behavior. A core component of this
+project was developing infrastructure and optimization methods that behave
+predictably across a wide range of scales. This allowed us to accurately
+predict some aspects of GPT-4's performance based on models trained with no
+more than 1/1,000th the compute of GPT-4.
+                
+## A Watermark for Large Language Models
+
+- **arXiv id:** 2301.10226v4
+- **Title:** A Watermark for Large Language Models
+- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
+- **Published Date:** 2023-01-24
+- **URL:** http://arxiv.org/abs/2301.10226v4
+- **LangChain:**
+
+   - **API Reference:** [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...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_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Potential harms of large language models can be mitigated by watermarking
 model output, i.e., embedding signals into generated text that are invisible to
@@ -645,11 +558,13 @@ family, and discuss robustness and security.
                 
 ## Precise Zero-Shot Dense Retrieval without Relevance Labels
 
+- **arXiv id:** 2212.10496v1
+- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
 - **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
-- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
+- **Published Date:** 2022-12-20
+- **URL:** http://arxiv.org/abs/2212.10496v1
 - **LangChain:**
 
-   - **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)
@@ -672,37 +587,13 @@ state-of-the-art unsupervised dense retriever Contriever and shows strong
 performance comparable to fine-tuned retrievers, across various tasks (e.g. web
 search, QA, fact verification) and languages~(e.g. sw, ko, ja).
                 
-## Constitutional AI: Harmlessness from AI Feedback
-
-- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al.
-- **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/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
-assistant through self-improvement, without any human labels identifying
-harmful outputs. The only human oversight is provided through a list of rules
-or principles, and so we refer to the method as 'Constitutional AI'. The
-process involves both a supervised learning and a reinforcement learning phase.
-In the supervised phase we sample from an initial model, then generate
-self-critiques and revisions, and then finetune the original model on revised
-responses. In the RL phase, we sample from the finetuned model, use a model to
-evaluate which of the two samples is better, and then train a preference model
-from this dataset of AI preferences. We then train with RL using the preference
-model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
-result we are able to train a harmless but non-evasive AI assistant that
-engages with harmful queries by explaining its objections to them. Both the SL
-and RL methods can leverage chain-of-thought style reasoning to improve the
-human-judged performance and transparency of AI decision making. These methods
-make it possible to control AI behavior more precisely and with far fewer human
-labels.
-                
 ## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
 
+- **arXiv id:** 2212.07425v3
+- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
 - **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
-- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
+- **Published Date:** 2022-12-12
+- **URL:** http://arxiv.org/abs/2212.07425v3
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
@@ -731,8 +622,11 @@ further work on logical fallacy identification.
                 
 ## Complementary Explanations for Effective In-Context Learning
 
+- **arXiv id:** 2211.13892v2
+- **Title:** Complementary Explanations for Effective In-Context Learning
 - **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
-- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
+- **Published Date:** 2022-11-25
+- **URL:** http://arxiv.org/abs/2211.13892v2
 - **LangChain:**
 
    - **API Reference:** [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)
@@ -756,11 +650,14 @@ performance across three real-world tasks on multiple LLMs.
                 
 ## PAL: Program-aided Language Models
 
+- **arXiv id:** 2211.10435v2
+- **Title:** PAL: Program-aided Language Models
 - **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
-- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
+- **Published Date:** 2022-11-18
+- **URL:** http://arxiv.org/abs/2211.10435v2
 - **LangChain:**
 
-   - **API Reference:** [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)
+   - **API Reference:** [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), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
    - **Cookbook:** [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
 
 **Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
@@ -786,64 +683,16 @@ accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
 which uses chain-of-thought by absolute 15% top-1. Our code and data are
 publicly available at http://reasonwithpal.com/ .
                 
-## An Analysis of Fusion Functions for Hybrid Retrieval
-
-- **Authors:** Sebastian Bruch, Siyu Gai, Amir Ingber
-- **arXiv id:** [2210.11934v2](http://arxiv.org/abs/2210.11934v2)  **Published Date:** 2022-10-21
-- **LangChain:**
-
-   - **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
-they model relevance. In particular, we examine fusion by a convex combination
-(CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion
-(RRF) method, and identify their advantages and potential pitfalls. Contrary to
-existing studies, we find RRF to be sensitive to its parameters; that the
-learning of a CC fusion is generally agnostic to the choice of score
-normalization; that CC outperforms RRF in in-domain and out-of-domain settings;
-and finally, that CC is sample efficient, requiring only a small set of
-training examples to tune its only parameter to a target domain.
-                
-## ReAct: Synergizing Reasoning and Acting in Language Models
-
-- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
-- **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/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
-across tasks in language understanding and interactive decision making, their
-abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g.
-action plan generation) have primarily been studied as separate topics. In this
-paper, we explore the use of LLMs to generate both reasoning traces and
-task-specific actions in an interleaved manner, allowing for greater synergy
-between the two: reasoning traces help the model induce, track, and update
-action plans as well as handle exceptions, while actions allow it to interface
-with external sources, such as knowledge bases or environments, to gather
-additional information. We apply our approach, named ReAct, to a diverse set of
-language and decision making tasks and demonstrate its effectiveness over
-state-of-the-art baselines, as well as improved human interpretability and
-trustworthiness over methods without reasoning or acting components.
-Concretely, on question answering (HotpotQA) and fact verification (Fever),
-ReAct overcomes issues of hallucination and error propagation prevalent in
-chain-of-thought reasoning by interacting with a simple Wikipedia API, and
-generates human-like task-solving trajectories that are more interpretable than
-baselines without reasoning traces. On two interactive decision making
-benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and
-reinforcement learning methods by an absolute success rate of 34% and 10%
-respectively, while being prompted with only one or two in-context examples.
-Project site with code: https://react-lm.github.io
-                
 ## Deep Lake: a Lakehouse for Deep Learning
 
+- **arXiv id:** 2209.10785v2
+- **Title:** Deep Lake: a Lakehouse for Deep Learning
 - **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
-- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
+- **Published Date:** 2022-09-22
+- **URL:** http://arxiv.org/abs/2209.10785v2
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
+   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/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
@@ -862,41 +711,13 @@ visualization engine, or (c) deep learning frameworks without sacrificing GPU
 utilization. Datasets stored in Deep Lake can be accessed from PyTorch,
 TensorFlow, JAX, and integrate with numerous MLOps tools.
                 
-## Matryoshka Representation Learning
-
-- **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al.
-- **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/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
-often the case that computational and statistical constraints for each
-downstream task are unknown. In this context rigid, fixed capacity
-representations can be either over or under-accommodating to the task at hand.
-This leads us to ask: can we design a flexible representation that can adapt to
-multiple downstream tasks with varying computational resources? Our main
-contribution is Matryoshka Representation Learning (MRL) which encodes
-information at different granularities and allows a single embedding to adapt
-to the computational constraints of downstream tasks. MRL minimally modifies
-existing representation learning pipelines and imposes no additional cost
-during inference and deployment. MRL learns coarse-to-fine representations that
-are at least as accurate and rich as independently trained low-dimensional
-representations. The flexibility within the learned Matryoshka Representations
-offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at
-the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale
-retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for
-long-tail few-shot classification, all while being as robust as the original
-representations. Finally, we show that MRL extends seamlessly to web-scale
-datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet),
-vision + language (ALIGN) and language (BERT). MRL code and pretrained models
-are open-sourced at https://github.com/RAIVNLab/MRL.
-                
 ## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 
+- **arXiv id:** 2205.12654v1
+- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 - **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
-- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
+- **Published Date:** 2022-05-25
+- **URL:** http://arxiv.org/abs/2205.12654v1
 - **LangChain:**
 
    - **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
@@ -921,12 +742,14 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
                 
 ## Evaluating the Text-to-SQL Capabilities of Large Language Models
 
+- **arXiv id:** 2204.00498v1
+- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
 - **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
-- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
+- **Published Date:** 2022-03-15
+- **URL:** http://arxiv.org/abs/2204.00498v1
 - **LangChain:**
 
-   - **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)
+   - **API Reference:** [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
 
 **Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
 language model. We find that, without any finetuning, Codex is a strong
@@ -938,11 +761,14 @@ few-shot examples.
                 
 ## Locally Typical Sampling
 
+- **arXiv id:** 2202.00666v5
+- **Title:** Locally Typical Sampling
 - **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
-- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
+- **Published Date:** 2022-02-01
+- **URL:** http://arxiv.org/abs/2202.00666v5
 - **LangChain:**
 
-   - **API Reference:** [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)
+   - **API Reference:** [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), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Today's probabilistic language generators fall short when it comes to
 producing coherent and fluent text despite the fact that the underlying models
@@ -965,32 +791,13 @@ locally typical sampling offers competitive performance (in both abstractive
 summarization and story generation) in terms of quality while consistently
 reducing degenerate repetitions.
                 
-## ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
-
-- **Authors:** Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al.
-- **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/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
-and documents into single-vector representations, late interaction models
-produce multi-vector representations at the granularity of each token and
-decompose relevance modeling into scalable token-level computations. This
-decomposition has been shown to make late interaction more effective, but it
-inflates the space footprint of these models by an order of magnitude. In this
-work, we introduce ColBERTv2, a retriever that couples an aggressive residual
-compression mechanism with a denoised supervision strategy to simultaneously
-improve the quality and space footprint of late interaction. We evaluate
-ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art
-quality within and outside the training domain while reducing the space
-footprint of late interaction models by 6--10$\times$.
-                
 ## Learning Transferable Visual Models From Natural Language Supervision
 
+- **arXiv id:** 2103.00020v1
+- **Title:** Learning Transferable Visual Models From Natural Language Supervision
 - **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
-- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
+- **Published Date:** 2021-02-26
+- **URL:** http://arxiv.org/abs/2103.00020v1
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
@@ -1016,77 +823,16 @@ zero-shot without needing to use any of the 1.28 million training examples it
 was trained on. We release our code and pre-trained model weights at
 https://github.com/OpenAI/CLIP.
                 
-## Language Models are Few-Shot Learners
-
-- **Authors:** Tom B. Brown, Benjamin Mann, Nick Ryder,  et al.
-- **arXiv id:** [2005.14165v4](http://arxiv.org/abs/2005.14165v4)  **Published Date:** 2020-05-28
-- **LangChain:**
-
-   - **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
-a specific task. While typically task-agnostic in architecture, this method
-still requires task-specific fine-tuning datasets of thousands or tens of
-thousands of examples. By contrast, humans can generally perform a new language
-task from only a few examples or from simple instructions - something which
-current NLP systems still largely struggle to do. Here we show that scaling up
-language models greatly improves task-agnostic, few-shot performance, sometimes
-even reaching competitiveness with prior state-of-the-art fine-tuning
-approaches. Specifically, we train GPT-3, an autoregressive language model with
-175 billion parameters, 10x more than any previous non-sparse language model,
-and test its performance in the few-shot setting. For all tasks, GPT-3 is
-applied without any gradient updates or fine-tuning, with tasks and few-shot
-demonstrations specified purely via text interaction with the model. GPT-3
-achieves strong performance on many NLP datasets, including translation,
-question-answering, and cloze tasks, as well as several tasks that require
-on-the-fly reasoning or domain adaptation, such as unscrambling words, using a
-novel word in a sentence, or performing 3-digit arithmetic. At the same time,
-we also identify some datasets where GPT-3's few-shot learning still struggles,
-as well as some datasets where GPT-3 faces methodological issues related to
-training on large web corpora. Finally, we find that GPT-3 can generate samples
-of news articles which human evaluators have difficulty distinguishing from
-articles written by humans. We discuss broader societal impacts of this finding
-and of GPT-3 in general.
-                
-## Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
-
-- **Authors:** Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al.
-- **arXiv id:** [2005.11401v4](http://arxiv.org/abs/2005.11401v4)  **Published Date:** 2020-05-22
-- **LangChain:**
-
-   - **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
-downstream NLP tasks. However, their ability to access and precisely manipulate
-knowledge is still limited, and hence on knowledge-intensive tasks, their
-performance lags behind task-specific architectures. Additionally, providing
-provenance for their decisions and updating their world knowledge remain open
-research problems. Pre-trained models with a differentiable access mechanism to
-explicit non-parametric memory can overcome this issue, but have so far been
-only investigated for extractive downstream tasks. We explore a general-purpose
-fine-tuning recipe for retrieval-augmented generation (RAG) -- models which
-combine pre-trained parametric and non-parametric memory for language
-generation. We introduce RAG models where the parametric memory is a
-pre-trained seq2seq model and the non-parametric memory is a dense vector index
-of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG
-formulations, one which conditions on the same retrieved passages across the
-whole generated sequence, the other can use different passages per token. We
-fine-tune and evaluate our models on a wide range of knowledge-intensive NLP
-tasks and set the state-of-the-art on three open domain QA tasks, outperforming
-parametric seq2seq models and task-specific retrieve-and-extract architectures.
-For language generation tasks, we find that RAG models generate more specific,
-diverse and factual language than a state-of-the-art parametric-only seq2seq
-baseline.
-                
 ## CTRL: A Conditional Transformer Language Model for Controllable Generation
 
+- **arXiv id:** 1909.05858v2
+- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
 - **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
-- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
+- **Published Date:** 2019-09-11
+- **URL:** http://arxiv.org/abs/1909.05858v2
 - **LangChain:**
 
-   - **API Reference:** [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)
+   - **API Reference:** [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), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Large-scale language models show promising text generation capabilities, but
 users cannot easily control particular aspects of the generated text. We
@@ -1099,4 +845,32 @@ codes also allow CTRL to predict which parts of the training data are most
 likely given a sequence. This provides a potential method for analyzing large
 amounts of data via model-based source attribution. We have released multiple
 full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
+                
+## Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
+
+- **arXiv id:** 1908.10084v1
+- **Title:** Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
+- **Authors:** Nils Reimers, Iryna Gurevych
+- **Published Date:** 2019-08-27
+- **URL:** http://arxiv.org/abs/1908.10084v1
+- **LangChain:**
+
+   - **Documentation:** [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
+
+**Abstract:** BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
+state-of-the-art performance on sentence-pair regression tasks like semantic
+textual similarity (STS). However, it requires that both sentences are fed into
+the network, which causes a massive computational overhead: Finding the most
+similar pair in a collection of 10,000 sentences requires about 50 million
+inference computations (~65 hours) with BERT. The construction of BERT makes it
+unsuitable for semantic similarity search as well as for unsupervised tasks
+like clustering.
+  In this publication, we present Sentence-BERT (SBERT), a modification of the
+pretrained BERT network that use siamese and triplet network structures to
+derive semantically meaningful sentence embeddings that can be compared using
+cosine-similarity. This reduces the effort for finding the most similar pair
+from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while
+maintaining the accuracy from BERT.
+  We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning
+tasks, where it outperforms other state-of-the-art sentence embeddings methods.
                 

docs/docs/additional_resources/tutorials.mdx

@@ -11,7 +11,6 @@
 ### [by Prompt Engineering](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr)
 ### [by Mayo Oshin](https://www.youtube.com/@chatwithdata/search?query=langchain)
 ### [by 1 little Coder](https://www.youtube.com/playlist?list=PLpdmBGJ6ELUK-v0MK-t4wZmVEbxM5xk6L)
-### [by BobLin (Chinese language)](https://www.youtube.com/playlist?list=PLbd7ntv6PxC3QMFQvtWfk55p-Op_syO1C)
 
 ## Courses
 
@@ -46,6 +45,7 @@
 - [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
 - [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
 - [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
-- [Dive into Langchain (Chinese language)](https://langchain.boblin.app/)
 
 ---------------------
+
+

docs/docs/contributing/code.mdx

@@ -1,9 +1,36 @@
-# Setup
+---
+sidebar_position: 1
+---
+# Contribute Code
 
-This guide walks through how to run the repository locally and check in your first code.
+To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
+Please do not try to push directly to this repo unless you are a maintainer.
+
+Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
+maintainers.
+
+Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
+[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
+
+It's essential that we maintain great documentation and testing. If you:
+- Fix a bug
+  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
+- Make an improvement
+  - Update any affected example notebooks and documentation. These live in `docs`.
+  - Update unit and integration tests when relevant.
+- Add a feature
+  - Add a demo notebook in `docs/docs/`.
+  - Add unit and integration tests.
+
+We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
+best way to get our attention.
+
+## 🚀 Quick Start
+
+This quick start guide explains how to run the repository locally.
 For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
 
-## Dependency Management: Poetry and other env/dependency managers
+### Dependency Management: Poetry and other env/dependency managers
 
 This project utilizes [Poetry](https://python-poetry.org/) v1.7.1+ as a dependency manager.
 
@@ -14,7 +41,7 @@ Install Poetry: **[documentation on how to install it](https://python-poetry.org
 ❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
 tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
 
-## Different packages
+### Different packages
 
 This repository contains multiple packages:
 - `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
@@ -32,7 +59,7 @@ For this quickstart, start with langchain-community:
 cd libs/community
 ```
 
-## Local Development Dependencies
+### Local Development Dependencies
 
 Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
 
@@ -52,9 +79,9 @@ If you are still seeing this bug on v1.6.1+, you may also try disabling "modern
 (`poetry config installer.modern-installation false`) and re-installing requirements.
 See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-## Testing
+### Testing
 
-**Note:** In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional. See the following section about optional dependencies.
+_In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional; see section about optional dependencies_.
 
 Unit tests cover modular logic that does not require calls to outside APIs.
 If you add new logic, please add a unit test.
@@ -91,11 +118,11 @@ poetry install --with test
 make test
 ```
 
-## Formatting and Linting
+### Formatting and Linting
 
 Run these locally before submitting a PR; the CI system will check also.
 
-### Code Formatting
+#### Code Formatting
 
 Formatting for this project is done via [ruff](https://docs.astral.sh/ruff/rules/).
 
@@ -147,7 +174,7 @@ This can be very helpful when you've made changes to only certain parts of the p
 
 We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
-### Spellcheck
+#### Spellcheck
 
 Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
 Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.

docs/docs/contributing/code/guidelines.mdx

@@ -1,35 +0,0 @@
-# General guidelines
-
-Here are some things to keep in mind for all types of contributions:
-
-- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
-- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
-- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
-  - See the sections on [Testing](/docs/contributing/code/setup#testing) and [Formatting and Linting](/docs/contributing/code/setup#formatting-and-linting) for how to run these checks locally.
-- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
-- Look for duplicate PRs or issues that have already been opened before opening a new one.
-- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
-
-## Bugfixes
-
-We encourage and appreciate bugfixes. We ask that you:
-
-- Explain the bug in enough detail for maintainers to be able to reproduce it.
-  - If an accompanying issue exists, link to it. Prefix with `Fixes` so that the issue will close automatically when the PR is merged.
-- Avoid breaking changes if possible.
-- Include unit tests that fail without the bugfix.
-
-If you come across a bug and don't know how to fix it, we ask that you open an issue for it describing in detail the environment in which you encountered the bug.
-
-## New features
-
-We aim to keep the bar high for new features. We generally don't accept new core abstractions, changes to infra, changes to dependencies,
-or new agents/chains from outside contributors without an existing GitHub discussion or issue that demonstrates an acute need for them.
-
-- New features must come with docs, unit tests, and (if appropriate) integration tests.
-- New integrations must come with docs, unit tests, and (if appropriate) integration tests.
-  - See [this page](/docs/contributing/integrations) for more details on contributing new integrations.
-- New functionality should not inherit from or use deprecated methods or classes.
-- We will reject features that are likely to lead to security vulnerabilities or reports.
-- Do not add any hard dependencies. Integrations may add optional dependencies.

docs/docs/contributing/code/index.mdx

@@ -1,6 +0,0 @@
-# Contribute Code
-
-If you would like to add a new feature or update an existing one, please read the resources below before getting started:
-
-- [General guidelines](/docs/contributing/code/guidelines/)
-- [Setup](/docs/contributing/code/setup/)

docs/docs/contributing/documentation/_category_.yml

@@ -0,0 +1,2 @@
+label: 'Documentation'
+position: 3

docs/docs/contributing/documentation/index.mdx

@@ -1,7 +0,0 @@
-# Contribute Documentation
-
-Documentation is a vital part of LangChain. We welcome both new documentation for new features and 
-community improvements to our current documentation. Please read the resources below before getting started:
-
-- [Documentation style guide](/docs/contributing/documentation/style_guide/)
-- [Setup](/docs/contributing/documentation/setup/)

docs/docs/contributing/documentation/style_guide.mdx

@@ -1,8 +1,10 @@
 ---
-sidebar_class_name: "hidden"
+sidebar_label: "Style guide"
 ---
 
-# Documentation Style Guide
+# LangChain Documentation Style Guide
+
+## Introduction
 
 As LangChain continues to grow, the surface area of documentation required to cover it continues to grow too.
 This page provides guidelines for anyone writing documentation for LangChain, as well as some of our philosophies around
@@ -10,139 +12,116 @@ organization and structure.
 
 ## Philosophy
 
-LangChain's documentation follows the [Diataxis framework](https://diataxis.fr).
-Under this framework, all documentation falls under one of four categories: [Tutorials](/docs/contributing/documentation/style_guide/#tutorials),
-[How-to guides](/docs/contributing/documentation/style_guide/#how-to-guides),
-[References](/docs/contributing/documentation/style_guide/#references), and [Explanations](/docs/contributing/documentation/style_guide/#conceptual-guide).
+LangChain's documentation aspires to follow the [Diataxis framework](https://diataxis.fr).
+Under this framework, all documentation falls under one of four categories:
 
-### Tutorials
-
-Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
-gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way. They should **avoid** giving
-multiple permutations of ways to achieve that goal in-depth. Instead, it should guide a new user through a recommended path to accomplishing the tutorial's goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction. Information on how to address additional scenarios
-belongs in how-to guides.
-
-To quote the Diataxis website:
-
-> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
-
-In LangChain, these are often higher level guides that show off end-to-end use cases.
-
-Some examples include:
-
-- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain/)
-- [Build a Retrieval Augmented Generation (RAG) App](/docs/tutorials/rag/)
-
-A good structural rule of thumb is to follow the structure of this [example from Numpy](https://numpy.org/numpy-tutorials/content/tutorial-svd.html).
-  
-Here are some high-level tips on writing a good tutorial:
-
-- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
-- Be specific, not abstract and follow one path.
-  - No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
-- Get "a point on the board" as soon as possible - something the user can run that outputs something.
-  - You can iterate and expand afterwards.
-  - Try to frequently checkpoint at given steps where the user can run code and see progress.
-- Focus on results, not technical explanation.
-  - Crosslink heavily to appropriate conceptual/reference pages.
-- The first time you mention a LangChain concept, use its full name (e.g. "LangChain Expression Language (LCEL)"), and link to its conceptual/other documentation page.
-  - It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
-  
-### How-to guides
-
-A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
-It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
-should still give some background or list the scenarios where the information contained within can be relevant.
-They can and should discuss alternatives if one approach may be better than another in certain cases.
-
-To quote the Diataxis website:
-
-> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
-
-Some examples include:
-
-- [How to: return structured data from a model](/docs/how_to/structured_output/)
-- [How to: write a custom chat model](/docs/how_to/custom_chat_model/)
-
-Here are some high-level tips on writing a good how-to guide:
-
-- Clearly explain what you are guiding the user through at the start.
-- Assume higher intent than a tutorial and show what the user needs to do to get that task done.
-- Assume familiarity of concepts, but explain why suggested actions are helpful.
-  - Crosslink heavily to conceptual/reference pages.
-- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem.
-- Use lots of example code.
-  - Prefer full code blocks that the reader can copy and run.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides.
-
-### Conceptual guide
-
-LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
-in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
-gaining a deeper understanding of the framework. Try to avoid excessively large code examples - the goal here is to
-impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
-
-This guide on documentation style is meant to fall under this category.
-
-To quote the Diataxis website:
-
-> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
-
-Some examples include:
-
-- [Retrieval conceptual docs](/docs/concepts/#retrieval)
-- [Chat model conceptual docs](/docs/concepts/#chat-models)
-
-Here are some high-level tips on writing a good conceptual guide:
-
-- Explain design decisions. Why does concept X exist and why was it designed this way?
-- Use analogies and reference other concepts and alternatives
-- Avoid blending in too much reference content
-- You can and should reference content covered in other guides, but make sure to link to them
-
-### References
-
-References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
-In LangChain, this is mainly our API reference pages, which are populated from docstrings within code.
-References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
-how to use something specific.
-
-To quote the Diataxis website:
-
-> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
-
-Many of the reference pages in LangChain are automatically generated from code,
-but here are some high-level tips on writing a good docstring:
-
-- Be concise
-- Discuss special cases and deviations from a user's expectations
-- Go into detail on required inputs and outputs
-- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
+- **Tutorials**: Lessons that take the reader by the hand through a series of conceptual steps to complete a project.
+  - An example of this is our [LCEL streaming guide](/docs/how_to/streaming).
+  - Our guides on [custom components](/docs/how_to/custom_chat_model) is another one.
+- **How-to guides**: Guides that take the reader through the steps required to solve a real-world problem.
+  - The clearest examples of this are our [Use case](/docs/how_to#use-cases) quickstart pages.
+- **Reference**: Technical descriptions of the machinery and how to operate it.
+  - Our [Runnable interface](/docs/concepts#interface) page is an example of this.
+  - The [API reference pages](https://api.python.langchain.com/) are another.
+- **Explanation**: Explanations that clarify and illuminate a particular topic.
+  - The [LCEL primitives pages](/docs/how_to/sequence) are an example of this.
 
 Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
 
-## General guidelines
+## Taxonomy
+
+Keeping the above in mind, we have sorted LangChain's docs into categories. It is helpful to think in these terms
+when contributing new documentation:
+
+### Getting started
+
+The [getting started section](/docs/introduction) includes a high-level introduction to LangChain, a quickstart that
+tours LangChain's various features, and logistical instructions around installation and project setup.
+
+It contains elements of **How-to guides** and **Explanations**.
+
+### Use cases
+
+[Use cases](/docs/how_to#use-cases) are guides that are meant to show how to use LangChain to accomplish a specific task (RAG, information extraction, etc.).
+The quickstarts should be good entrypoints for first-time LangChain developers who prefer to learn by getting something practical prototyped,
+then taking the pieces apart retrospectively. These should mirror what LangChain is good at.
+
+The quickstart pages here should fit the **How-to guide** category, with the other pages intended to be **Explanations** of more
+in-depth concepts and strategies that accompany the main happy paths.
+
+:::note
+The below sections are listed roughly in order of increasing level of abstraction.
+:::
+
+### Expression Language
+
+[LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language-lcel) is the fundamental way that most LangChain components fit together, and this section is designed to teach
+developers how to use it to build with LangChain's primitives effectively.
+
+This section should contains **Tutorials** that teach how to stream and use LCEL primitives for more abstract tasks, **Explanations** of specific behaviors,
+and some **References** for how to use different methods in the Runnable interface.
+
+### Components
+
+The [components section](/docs/concepts) covers concepts one level of abstraction higher than LCEL.
+Abstract base classes like `BaseChatModel` and `BaseRetriever` should be covered here, as well as core implementations of these base classes,
+such as `ChatPromptTemplate` and `RecursiveCharacterTextSplitter`. Customization guides belong here too.
+
+This section should contain mostly conceptual **Tutorials**, **References**, and **Explanations** of the components they cover.
+
+:::note
+As a general rule of thumb, everything covered in the `Expression Language` and `Components` sections (with the exception of the `Composition` section of components) should
+cover only components that exist in `langchain_core`.
+:::
+
+### Integrations
+
+The [integrations](/docs/integrations/platforms/) are specific implementations of components. These often involve third-party APIs and services.
+If this is the case, as a general rule, these are maintained by the third-party partner.
+
+This section should contain mostly **Explanations** and **References**, though the actual content here is more flexible than other sections and more at the
+discretion of the third-party provider.
+
+:::note
+Concepts covered in `Integrations` should generally exist in `langchain_community` or specific partner packages.
+:::
+
+### Guides and Ecosystem
+
+The [Guides](/docs/tutorials) and [Ecosystem](https://docs.smith.langchain.com/) sections should contain guides that address higher-level problems than the sections above.
+This includes, but is not limited to, considerations around productionization and development workflows.
+
+These should contain mostly **How-to guides**, **Explanations**, and **Tutorials**.
+
+### API references
+
+LangChain's API references. Should act as **References** (as the name implies) with some **Explanation**-focused content as well. 
+
+## Sample developer journey
+
+We have set up our docs to assist a new developer to LangChain. Let's walk through the intended path:
+
+- The developer lands on https://python.langchain.com, and reads through the introduction and the diagram.
+- If they are just curious, they may be drawn to the [Quickstart](/docs/tutorials/llm_chain) to get a high-level tour of what LangChain contains.
+- If they have a specific task in mind that they want to accomplish, they will be drawn to the Use-Case section. The use-case should provide a good, concrete hook that shows the value LangChain can provide them and be a good entrypoint to the framework.
+- They can then move to learn more about the fundamentals of LangChain through the Expression Language sections.
+- Next, they can learn about LangChain's various components and integrations.
+- Finally, they can get additional knowledge through the Guides.
+
+This is only an ideal of course - sections will inevitably reference lower or higher-level concepts that are documented in other sections.
+
+## Guidelines
 
 Here are some other guidelines you should think about when writing and organizing documentation.
 
-We generally do not merge new tutorials from outside contributors without an actue need.
-We welcome updates as well as new integration docs, how-tos, and references.
-
-### Avoid duplication
-
-Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
-be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
-
-### Link to other sections
+### Linking to other sections
 
 Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
 to allow a developer to learn more about an unfamiliar topic inline.
 
 This includes linking to the API references as well as conceptual sections!
 
-### Be concise
+### Conciseness
 
 In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
 re-explain it, unless the concept you are documenting presents some new wrinkle.
@@ -151,10 +130,9 @@ Be concise, including in code samples.
 
 ### General style
 
-- Use active voice and present tense whenever possible
-- Use examples and code snippets to illustrate concepts and usage
-- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
-- Use fewer cells with more code to make copy/paste easier
-- Use bullet points and numbered lists to break down information into easily digestible chunks
-- Use tables (especially for **Reference** sections) and diagrams often to present information visually
-- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
+- Use active voice and present tense whenever possible.
+- Use examples and code snippets to illustrate concepts and usage.
+- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically.
+- Use bullet points and numbered lists to break down information into easily digestible chunks.
+- Use tables (especially for **Reference** sections) and diagrams often to present information visually.
+- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages.

docs/docs/contributing/documentation/technical_logistics.mdx

@@ -1,8 +1,4 @@
----
-sidebar_class_name: "hidden"
----
-
-# Setup
+# Technical logistics
 
 LangChain documentation consists of two components:
 
@@ -12,10 +8,12 @@ It covers a wide array of topics, including tutorials, use cases, integrations,
 and more, offering extensive guidance on building with LangChain.
 The content for this documentation lives in the `/docs` directory of the monorepo.
 2. In-code Documentation: This is documentation of the codebase itself, which is also
-used to generate the externally facing [API Reference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).
+used to generate the externally facing [API Reference](https://api.python.langchain.com/en/latest/langchain_api_reference.html).
 The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
 developers document their code well.
 
+The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
+
 The `API Reference` is largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/)
 from the code and is hosted by [Read the Docs](https://readthedocs.org/).
 
@@ -31,7 +29,7 @@ The content for the main documentation is located in the `/docs` directory of th
 
 The documentation is written using a combination of ipython notebooks (`.ipynb` files)
 and markdown (`.mdx` files). The notebooks are converted to markdown
-and then built using [Docusaurus 2](https://docusaurus.io/).
+using [Quarto](https://quarto.org) and then built using [Docusaurus 2](https://docusaurus.io/).
 
 Feel free to make contributions to the main documentation! 🥰
 
@@ -50,6 +48,10 @@ locally to ensure that it looks good and is free of errors.
 If you're unable to build it locally that's okay as well, as you will be able to
 see a preview of the documentation on the pull request page.
 
+### Install dependencies
+
+- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. [Download link](https://quarto.org/docs/download/).
+
 From the **monorepo root**, run the following command to install the dependencies:
 
 ```bash
@@ -69,6 +71,8 @@ make docs_clean
 make api_docs_clean
 ```
 
+
+
 Next, you can build the documentation as outlined below:
 
 ```bash

docs/docs/contributing/faq.mdx

@@ -24,16 +24,3 @@ for more information.
 Notably, Github doesn't allow this setting to be enabled for forks in **organizations** ([issue](https://github.com/orgs/community/discussions/5634)).
 If you are working in an organization, we recommend submitting your PR from a personal
 fork in order to enable this setting.
-
-### Why hasn't my PR been reviewed?
-
-Please reference our [Review Process](/docs/contributing/review_process/).
-
-### Why was my PR closed?
-
-Please reference our [Review Process](/docs/contributing/review_process/).
-
-### I think my PR was closed in a way that didn't follow the review process. What should I do?
-
-Tag `@efriis` in the PR comments referencing the portion of the review
-process that you believe was not followed. We'll take a look!

docs/docs/contributing/index.mdx

@@ -12,8 +12,8 @@ As an open-source project in a rapidly developing field, we are extremely open t
 
 There are many ways to contribute to LangChain. Here are some common ways people contribute:
 
-- [**Documentation**](/docs/contributing/documentation/): Help improve our docs, including this one!
-- [**Code**](/docs/contributing/code/): Help us write code, fix bugs, or improve our infrastructure.
+- [**Documentation**](/docs/contributing/documentation/style_guide): Help improve our docs, including this one!
+- [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
 - [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
 
@@ -48,7 +48,7 @@ In a similar vein, we do enforce certain linting, formatting, and documentation
 If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
 we do not want these to get in the way of getting good code into the codebase.
 
-### 🌟 Recognition
+# 🌟 Recognition
 
 If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
+If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.

docs/docs/contributing/integrations.mdx

@@ -1,7 +1,6 @@
 ---
 sidebar_position: 5
 ---
-
 # Contribute Integrations
 
 To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](/docs/contributing/code/).
@@ -11,7 +10,7 @@ There are a few different places you can contribute integrations for LangChain:
 - **Community**: For lighter-weight integrations that are primarily maintained by LangChain and the Open Source Community.
 - **Partner Packages**: For independent packages that are co-maintained by LangChain and a partner.
 
-For the most part, **new integrations should be added to the Community package**. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.
+For the most part, new integrations should be added to the Community package. Partner packages require more maintenance as separate packages, so please confirm with the LangChain team before creating a new partner package.
 
 In the following sections, we'll walk through how to contribute to each of these packages from a fake company, `Parrot Link AI`.
 
@@ -60,10 +59,6 @@ And add documentation to:
 
 ## Partner package in LangChain repo
 
-:::caution
-Before starting a **partner** package, please confirm your intent with the LangChain team. Partner packages require more maintenance as separate packages, so we will close PRs that add new partner packages without prior discussion. See the above section for how to add a community integration.
-:::
-
 Partner packages can be hosted in the `LangChain` monorepo or in an external repo.
 
 Partner package in the `LangChain` repo is placed in `libs/partners/{partner}` 

docs/docs/contributing/repo_structure.mdx

@@ -7,7 +7,6 @@ If you plan on contributing to LangChain code or documentation, it can be useful
 to understand the high level structure of the repository.
 
 LangChain is organized as a [monorepo](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
-You can check out our [installation guide](/docs/how_to/installation/) for more on how they fit together.
 
 Here's the structure visualized as a tree:
 
@@ -16,22 +15,12 @@ Here's the structure visualized as a tree:
 ├── cookbook # Tutorials and examples
 ├── docs # Contains content for the documentation here: https://python.langchain.com/
 ├── libs
-│   ├── langchain
-│   │   ├── langchain
+│   ├── langchain # Main package
 │   │   ├── tests/unit_tests # Unit tests (present in each package not shown for brevity)
 │   │   ├── tests/integration_tests # Integration tests (present in each package not shown for brevity)
-│   ├── community # Third-party integrations
-│   │   ├── langchain-community
-│   ├── core # Base interfaces for key abstractions
-│   │   ├── langchain-core
-│   ├── experimental # Experimental components and chains
-│   │   ├── langchain-experimental
-|   ├── cli # Command line interface
-│   │   ├── langchain-cli
-│   ├── text-splitters
-│   │   ├── langchain-text-splitters
-│   ├── standard-tests
-│   │   ├── langchain-standard-tests
+│   ├── langchain-community # Third-party integrations
+│   ├── langchain-core # Base interfaces for key abstractions
+│   ├── langchain-experimental # Experimental components and chains
 │   ├── partners
 │       ├── langchain-partner-1
 │       ├── langchain-partner-2
@@ -50,9 +39,9 @@ There are other files in the root directory level, but their presence should be
 ## Documentation
 
 The `/docs` directory contains the content for the documentation that is shown
-at https://python.langchain.com/ and the associated API Reference https://python.langchain.com/v0.2/api_reference/langchain/index.html.
+at https://python.langchain.com/ and the associated API Reference https://api.python.langchain.com/en/latest/langchain_api_reference.html.
 
-See the [documentation](/docs/contributing/documentation/) guidelines to learn how to contribute to the documentation.
+See the [documentation](/docs/contributing/documentation/style_guide) guidelines to learn how to contribute to the documentation.
 
 ## Code
 
@@ -60,6 +49,6 @@ The `/libs` directory contains the code for the LangChain packages.
 
 To learn more about how to contribute code see the following guidelines:
 
-- [Code](/docs/contributing/code/): Learn how to develop in the LangChain codebase.
-- [Integrations](./integrations.mdx): Learn how to contribute to third-party integrations to `langchain-community` or to start a new partner package.
-- [Testing](./testing.mdx): Guidelines to learn how to write tests for the packages.
+- [Code](./code.mdx) Learn how to develop in the LangChain codebase.
+- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package.
+- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages.

docs/docs/contributing/review_process.mdx

@@ -1,95 +0,0 @@
-# Review Process
-
-## Overview
-
-This document outlines the process used by the LangChain maintainers for reviewing pull requests (PRs). The primary objective of this process is to enhance the LangChain developer experience.
-
-## Review Statuses
-
-We categorize PRs using three main statuses, which are marked as project item statuses in the right sidebar and can be viewed in detail [here](https://github.com/orgs/langchain-ai/projects/12/views/1).
-
-- **Triage**: 
-  - Initial status for all newly submitted PRs.
-  - Requires a maintainer to categorize it into one of the other statuses.
-
-- **Needs Support**:
-  - PRs that require community feedback or additional input before moving forward.
-  - Automatically promoted to the backlog if it receives 5 upvotes.
-  - An auto-comment is generated when this status is applied, explaining the flow and the upvote requirement.
-  - If the PR remains in this status for 25 days, it will be marked as “stale” via auto-comment.
-  - PRs will be auto-closed after 30 days if no further action is taken.
-
-- **In Review**:
-  - PRs that are actively under review by our team.
-  - These are regularly reviewed and monitored.
-
-**Note:** A PR may only have one status at a time.
-
-**Note:** You may notice 3 additional statuses of Done, Closed, and Internal that
-are external to this lifecycle. Done and Closed PRs have been merged or closed,
-respectively. Internal is for PRs submitted by core maintainers, and these PRs are owned
-by the submitter.
-
-## Review Guidelines
-
-1. **PRs that touch /libs/core**:
-   - PRs that directly impact core code and are likely to affect end users.
-   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
-   - These PRs are given top priority and are reviewed the fastest.
-   - PRs that don't have a **concise** descriptions of their motivation (either in PR summary of in a linked issue) are likely to be closed without an in-depth review. Please do not generate verbose PR descriptions with an LLM.
-   - PRs that don't have unit tests are likely to be closed.
-   - Feature requests should first be opened as a GitHub issue and discussed with the LangChain maintainers. Large PRs submitted without prior discussion are likely to be closed.
-
-2. **PRs that touch /libs/langchain**:
-   - High-impact PRs that are closely related to core PRs but slightly lower in priority.
-   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
-   - These are reviewed and closed aggressively, similar to core PRs.
-   - New feature requests should be discussed with the core maintainer team beforehand in an issue.
-
-3. **PRs that touch /libs/partners/****:
-   - PRs involving integration packages.
-   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
-   - The review may be conducted by our team or handed off to the partner's development team, depending on the PR's content.
-   - We maintain communication lines with most partner dev teams to facilitate this process.
-
-4. **Community PRs**:
-   - Most community PRs will get an initial status of "needs support".
-   - **Triage Guideline**: most PRs should go to `Needs support`. Bugfixes on high-traffic integrations should go straight to `In review`.
-   - **Triage Guideline**: all new features and integrations should go to `Needs support` and will be closed if they do not get enough support (measured by upvotes or comments).
-   - PRs in the `Needs Support` status for 20 days are marked as “stale” and will be closed after 30 days if no action is taken.
-
-5. **Documentation PRs**:
-   - PRs that touch the documentation content in docs/docs.
-   - **Triage Guideline**:
-      - PRs that fix typos or small errors in a single file and pass CI should go straight to `In Review`.
-      - PRs that make changes that have been discussed and agreed upon in an issue should go straight to `In Review`.
-      - PRs that add new pages or change the structure of the documentation should go to `Needs Support`.
-   - We strive to standardize documentation formats to streamline the review process.
-   - CI jobs run against documentation to ensure adherence to standards, automating much of the review.
-
-6. **PRs must be in English**:
-   - PRs that are not in English will be closed without review.
-   - This is to ensure that all maintainers can review the PRs effectively.
-
-## How to see a PR's status
-
-See screenshot:
-
-![PR Status](/img/review_process_status.png)
-
-*To see the status of all open PRs, please visit the [LangChain Project Board](https://github.com/orgs/langchain-ai/projects/12/views/2).*
-
-## Review Prioritization
-
-Our goal is to provide the best possible development experience by focusing on making software that:
-
-- Works: Works as intended (is bug-free).
-- Is useful: Improves LLM app development with components that work off-the-shelf and runtimes that simplify app building.
-- Is easy: Is intuitive to use and well-documented.
-
-We believe this process reflects our priorities and are open to feedback if you feel it does not.
-
-## Github Discussion
-
-We welcome your feedback on this process. Please feel free to add a comment in 
-[this GitHub Discussion](https://github.com/langchain-ai/langchain/discussions/25920).

docs/docs/contributing/testing.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 6
+sidebar_position: 2
 ---
 
 # Testing