Merge branch 'master' into erick/test-partner-failure

Thank you for contributing to LangChain!

- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
  - Example: "community: add foobar LLM"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.

.github/CONTRIBUTING.md

@@ -3,43 +3,4 @@
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
-To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
-
-## 🗺️ Guidelines
-
-### 👩‍💻 Ways to contribute
-
-There are many ways to contribute to LangChain. Here are some common ways people contribute:
-
-- [**Documentation**](https://python.langchain.com/docs/contributing/documentation): Help improve our docs, including this one!
-- [**Code**](https://python.langchain.com/docs/contributing/code): Help us write code, fix bugs, or improve our infrastructure.
-- [**Integrations**](https://python.langchain.com/docs/contributing/integrations): Help us integrate with your favorite vendors and tools.
-
-### 🚩GitHub Issues
-
-Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
-
-There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues.
-
-If you start working on an issue, please assign it to yourself.
-
-If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
-If two issues are related, or blocking, please link them rather than combining them.
-
-We will try to keep these issues as up-to-date as possible, though
-with the rapid rate of development in this field some may get out of date.
-If you notice this happening, please let us know.
-
-### 🙋Getting Help
-
-Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
-smooth for future contributors.
-
-In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
-we do not want these to get in the way of getting good code into the codebase.
-
-### Contributor Documentation
-
-To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
+To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/).

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,19 +1,24 @@
 Thank you for contributing to LangChain!
 
-Checklist:
-
-- [ ] PR title: Please title your PR "package: description", where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
+- [ ] **PR title**: "package: description"
+  - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
   - Example: "community: add foobar LLM"
-- [ ] PR message: **Delete this entire template message** and replace it with the following bulleted list
+
+
+- [ ] **PR message**: ***Delete this entire checklist*** and replace with
     - **Description:** a description of the change
     - **Issue:** the issue # it fixes, if applicable
     - **Dependencies:** any dependencies required for this change
     - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
-- [ ] Pass lint and test: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified to check that you're passing lint and testing. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
-- [ ] Add tests and docs: If you're adding a new integration, please include
+
+
+- [ ] **Add tests and docs**: If you're adding a new integration, please include
   1. a test for the integration, preferably unit tests that do not rely on network access,
   2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
 
+
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/
+
 Additional guidelines:
 - Make sure optional dependencies are imported within a function.
 - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.

.github/actions/people/Dockerfile

@@ -0,0 +1,7 @@
+FROM python:3.9
+
+RUN pip install httpx PyGithub "pydantic==2.0.2" pydantic-settings "pyyaml>=5.3.1,<6.0.0"
+
+COPY ./app /app
+
+CMD ["python", "/app/main.py"]

.github/actions/people/action.yml

@@ -0,0 +1,11 @@
+# Adapted from https://github.com/tiangolo/fastapi/blob/master/.github/actions/people/action.yml
+name: "Generate LangChain People"
+description: "Generate the data for the LangChain People page"
+author: "Jacob Lee <jacob@langchain.dev>"
+inputs:
+  token:
+    description: 'User token, to read the GitHub API. Can be passed in using {{ secrets.LANGCHAIN_PEOPLE_GITHUB_TOKEN }}'
+    required: true
+runs:
+  using: 'docker'
+  image: 'Dockerfile'

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

@@ -0,0 +1,641 @@
+# Adapted from https://github.com/tiangolo/fastapi/blob/master/.github/actions/people/app/main.py
+
+import logging
+import subprocess
+import sys
+from collections import Counter
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any, Container, Dict, List, Set, Union
+
+import httpx
+import yaml
+from github import Github
+from pydantic import BaseModel, SecretStr
+from pydantic_settings import BaseSettings
+
+github_graphql_url = "https://api.github.com/graphql"
+questions_category_id = "DIC_kwDOIPDwls4CS6Ve"
+
+# discussions_query = """
+# query Q($after: String, $category_id: ID) {
+#   repository(name: "langchain", owner: "langchain-ai") {
+#     discussions(first: 100, after: $after, categoryId: $category_id) {
+#       edges {
+#         cursor
+#         node {
+#           number
+#           author {
+#             login
+#             avatarUrl
+#             url
+#           }
+#           title
+#           createdAt
+#           comments(first: 100) {
+#             nodes {
+#               createdAt
+#               author {
+#                 login
+#                 avatarUrl
+#                 url
+#               }
+#               isAnswer
+#               replies(first: 10) {
+#                 nodes {
+#                   createdAt
+#                   author {
+#                     login
+#                     avatarUrl
+#                     url
+#                   }
+#                 }
+#               }
+#             }
+#           }
+#         }
+#       }
+#     }
+#   }
+# }
+# """
+
+# issues_query = """
+# query Q($after: String) {
+#   repository(name: "langchain", owner: "langchain-ai") {
+#     issues(first: 100, after: $after) {
+#       edges {
+#         cursor
+#         node {
+#           number
+#           author {
+#             login
+#             avatarUrl
+#             url
+#           }
+#           title
+#           createdAt
+#           state
+#           comments(first: 100) {
+#             nodes {
+#               createdAt
+#               author {
+#                 login
+#                 avatarUrl
+#                 url
+#               }
+#             }
+#           }
+#         }
+#       }
+#     }
+#   }
+# }
+# """
+
+prs_query = """
+query Q($after: String) {
+  repository(name: "langchain", owner: "langchain-ai") {
+    pullRequests(first: 100, after: $after, states: MERGED) {
+      edges {
+        cursor
+        node {
+          changedFiles
+          additions
+          deletions
+          number
+          labels(first: 100) {
+            nodes {
+              name
+            }
+          }
+          author {
+            login
+            avatarUrl
+            url
+            ... on User {
+              twitterUsername
+            }
+          }
+          title
+          createdAt
+          state
+          reviews(first:100) {
+            nodes {
+              author {
+                login
+                avatarUrl
+                url
+                ... on User {
+                  twitterUsername
+                }
+              }
+              state
+            }
+          }
+        }
+      }
+    }
+  }
+}
+"""
+
+
+class Author(BaseModel):
+    login: str
+    avatarUrl: str
+    url: str
+    twitterUsername: Union[str, None] = None
+
+
+# Issues and Discussions
+
+
+class CommentsNode(BaseModel):
+    createdAt: datetime
+    author: Union[Author, None] = None
+
+
+class Replies(BaseModel):
+    nodes: List[CommentsNode]
+
+
+class DiscussionsCommentsNode(CommentsNode):
+    replies: Replies
+
+
+class Comments(BaseModel):
+    nodes: List[CommentsNode]
+
+
+class DiscussionsComments(BaseModel):
+    nodes: List[DiscussionsCommentsNode]
+
+
+class IssuesNode(BaseModel):
+    number: int
+    author: Union[Author, None] = None
+    title: str
+    createdAt: datetime
+    state: str
+    comments: Comments
+
+
+class DiscussionsNode(BaseModel):
+    number: int
+    author: Union[Author, None] = None
+    title: str
+    createdAt: datetime
+    comments: DiscussionsComments
+
+
+class IssuesEdge(BaseModel):
+    cursor: str
+    node: IssuesNode
+
+
+class DiscussionsEdge(BaseModel):
+    cursor: str
+    node: DiscussionsNode
+
+
+class Issues(BaseModel):
+    edges: List[IssuesEdge]
+
+
+class Discussions(BaseModel):
+    edges: List[DiscussionsEdge]
+
+
+class IssuesRepository(BaseModel):
+    issues: Issues
+
+
+class DiscussionsRepository(BaseModel):
+    discussions: Discussions
+
+
+class IssuesResponseData(BaseModel):
+    repository: IssuesRepository
+
+
+class DiscussionsResponseData(BaseModel):
+    repository: DiscussionsRepository
+
+
+class IssuesResponse(BaseModel):
+    data: IssuesResponseData
+
+
+class DiscussionsResponse(BaseModel):
+    data: DiscussionsResponseData
+
+
+# PRs
+
+
+class LabelNode(BaseModel):
+    name: str
+
+
+class Labels(BaseModel):
+    nodes: List[LabelNode]
+
+
+class ReviewNode(BaseModel):
+    author: Union[Author, None] = None
+    state: str
+
+
+class Reviews(BaseModel):
+    nodes: List[ReviewNode]
+
+
+class PullRequestNode(BaseModel):
+    number: int
+    labels: Labels
+    author: Union[Author, None] = None
+    changedFiles: int
+    additions: int
+    deletions: int
+    title: str
+    createdAt: datetime
+    state: str
+    reviews: Reviews
+    # comments: Comments
+
+
+class PullRequestEdge(BaseModel):
+    cursor: str
+    node: PullRequestNode
+
+
+class PullRequests(BaseModel):
+    edges: List[PullRequestEdge]
+
+
+class PRsRepository(BaseModel):
+    pullRequests: PullRequests
+
+
+class PRsResponseData(BaseModel):
+    repository: PRsRepository
+
+
+class PRsResponse(BaseModel):
+    data: PRsResponseData
+
+
+class Settings(BaseSettings):
+    input_token: SecretStr
+    github_repository: str
+    httpx_timeout: int = 30
+
+
+def get_graphql_response(
+    *,
+    settings: Settings,
+    query: str,
+    after: Union[str, None] = None,
+    category_id: Union[str, None] = None,
+) -> Dict[str, Any]:
+    headers = {"Authorization": f"token {settings.input_token.get_secret_value()}"}
+    # category_id is only used by one query, but GraphQL allows unused variables, so
+    # keep it here for simplicity
+    variables = {"after": after, "category_id": category_id}
+    response = httpx.post(
+        github_graphql_url,
+        headers=headers,
+        timeout=settings.httpx_timeout,
+        json={"query": query, "variables": variables, "operationName": "Q"},
+    )
+    if response.status_code != 200:
+        logging.error(
+            f"Response was not 200, after: {after}, category_id: {category_id}"
+        )
+        logging.error(response.text)
+        raise RuntimeError(response.text)
+    data = response.json()
+    if "errors" in data:
+        logging.error(f"Errors in response, after: {after}, category_id: {category_id}")
+        logging.error(data["errors"])
+        logging.error(response.text)
+        raise RuntimeError(response.text)
+    return data
+
+
+# def get_graphql_issue_edges(*, settings: Settings, after: Union[str, None] = None):
+#     data = get_graphql_response(settings=settings, query=issues_query, after=after)
+#     graphql_response = IssuesResponse.model_validate(data)
+#     return graphql_response.data.repository.issues.edges
+
+
+# def get_graphql_question_discussion_edges(
+#     *,
+#     settings: Settings,
+#     after: Union[str, None] = None,
+# ):
+#     data = get_graphql_response(
+#         settings=settings,
+#         query=discussions_query,
+#         after=after,
+#         category_id=questions_category_id,
+#     )
+#     graphql_response = DiscussionsResponse.model_validate(data)
+#     return graphql_response.data.repository.discussions.edges
+
+
+def get_graphql_pr_edges(*, settings: Settings, after: Union[str, None] = None):
+    if after is None:
+        print("Querying PRs...")
+    else:
+        print(f"Querying PRs with cursor {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
+
+
+# def get_issues_experts(settings: Settings):
+#     issue_nodes: List[IssuesNode] = []
+#     issue_edges = get_graphql_issue_edges(settings=settings)
+
+#     while issue_edges:
+#         for edge in issue_edges:
+#             issue_nodes.append(edge.node)
+#         last_edge = issue_edges[-1]
+#         issue_edges = get_graphql_issue_edges(settings=settings, after=last_edge.cursor)
+
+#     commentors = Counter()
+#     last_month_commentors = Counter()
+#     authors: Dict[str, Author] = {}
+
+#     now = datetime.now(tz=timezone.utc)
+#     one_month_ago = now - timedelta(days=30)
+
+#     for issue in issue_nodes:
+#         issue_author_name = None
+#         if issue.author:
+#             authors[issue.author.login] = issue.author
+#             issue_author_name = issue.author.login
+#         issue_commentors = set()
+#         for comment in issue.comments.nodes:
+#             if comment.author:
+#                 authors[comment.author.login] = comment.author
+#                 if comment.author.login != issue_author_name:
+#                     issue_commentors.add(comment.author.login)
+#         for author_name in issue_commentors:
+#             commentors[author_name] += 1
+#             if issue.createdAt > one_month_ago:
+#                 last_month_commentors[author_name] += 1
+
+#     return commentors, last_month_commentors, authors
+
+
+# def get_discussions_experts(settings: Settings):
+#     discussion_nodes: List[DiscussionsNode] = []
+#     discussion_edges = get_graphql_question_discussion_edges(settings=settings)
+
+#     while discussion_edges:
+#         for discussion_edge in discussion_edges:
+#             discussion_nodes.append(discussion_edge.node)
+#         last_edge = discussion_edges[-1]
+#         discussion_edges = get_graphql_question_discussion_edges(
+#             settings=settings, after=last_edge.cursor
+#         )
+
+#     commentors = Counter()
+#     last_month_commentors = Counter()
+#     authors: Dict[str, Author] = {}
+
+#     now = datetime.now(tz=timezone.utc)
+#     one_month_ago = now - timedelta(days=30)
+
+#     for discussion in discussion_nodes:
+#         discussion_author_name = None
+#         if discussion.author:
+#             authors[discussion.author.login] = discussion.author
+#             discussion_author_name = discussion.author.login
+#         discussion_commentors = set()
+#         for comment in discussion.comments.nodes:
+#             if comment.author:
+#                 authors[comment.author.login] = comment.author
+#                 if comment.author.login != discussion_author_name:
+#                     discussion_commentors.add(comment.author.login)
+#             for reply in comment.replies.nodes:
+#                 if reply.author:
+#                     authors[reply.author.login] = reply.author
+#                     if reply.author.login != discussion_author_name:
+#                         discussion_commentors.add(reply.author.login)
+#         for author_name in discussion_commentors:
+#             commentors[author_name] += 1
+#             if discussion.createdAt > one_month_ago:
+#                 last_month_commentors[author_name] += 1
+#     return commentors, last_month_commentors, authors
+
+
+# def get_experts(settings: Settings):
+#     (
+#         discussions_commentors,
+#         discussions_last_month_commentors,
+#         discussions_authors,
+#     ) = get_discussions_experts(settings=settings)
+#     commentors = discussions_commentors
+#     last_month_commentors = discussions_last_month_commentors
+#     authors = {**discussions_authors}
+#     return commentors, last_month_commentors, authors
+
+
+def _logistic(x, k):
+    return x / (x + k)
+
+
+def get_contributors(settings: Settings):
+    pr_nodes: List[PullRequestNode] = []
+    pr_edges = get_graphql_pr_edges(settings=settings)
+
+    while pr_edges:
+        for edge in pr_edges:
+            pr_nodes.append(edge.node)
+        last_edge = pr_edges[-1]
+        pr_edges = get_graphql_pr_edges(settings=settings, after=last_edge.cursor)
+
+    contributors = Counter()
+    contributor_scores = Counter()
+    recent_contributor_scores = Counter()
+    reviewers = Counter()
+    authors: Dict[str, Author] = {}
+
+    for pr in pr_nodes:
+        pr_reviewers: Set[str] = set()
+        for review in pr.reviews.nodes:
+            if review.author:
+                authors[review.author.login] = review.author
+                pr_reviewers.add(review.author.login)
+        for reviewer in pr_reviewers:
+            reviewers[reviewer] += 1
+        if pr.author:
+            authors[pr.author.login] = pr.author
+            contributors[pr.author.login] += 1
+            files_changed = pr.changedFiles
+            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))
+            if pr.createdAt > three_months_ago:
+                recent_contributor_scores[pr.author.login] += score
+    return contributors, contributor_scores, recent_contributor_scores, reviewers, authors
+
+
+def get_top_users(
+    *,
+    counter: Counter,
+    min_count: int,
+    authors: Dict[str, Author],
+    skip_users: Container[str],
+):
+    users = []
+    for commentor, count in counter.most_common():
+        if commentor in skip_users:
+            continue
+        if count >= min_count:
+            author = authors[commentor]
+            users.append(
+                {
+                    "login": commentor,
+                    "count": count,
+                    "avatarUrl": author.avatarUrl,
+                    "twitterUsername": author.twitterUsername,
+                    "url": author.url,
+                }
+            )
+    return users
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    settings = Settings()
+    logging.info(f"Using config: {settings.model_dump_json()}")
+    g = Github(settings.input_token.get_secret_value())
+    repo = g.get_repo(settings.github_repository)
+    # 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
+    )
+    # authors = {**question_authors, **pr_authors}
+    authors = {**pr_authors}
+    maintainers_logins = {
+        "hwchase17",
+        "agola11",
+        "baskaryan",
+        "hinthornw",
+        "nfcampos",
+        "efriis",
+        "eyurtsev",
+        "rlancemartin"
+    }
+    hidden_logins = {
+        "dev2049",
+        "vowelparrot",
+        "obi1kenobi",
+        "langchain-infra",
+        "jacoblee93",
+        "dqbd",
+        "bracesproul",
+        "akira",
+    }
+    bot_names = {"dosubot", "github-actions", "CodiumAI-Agent"}
+    maintainers = []
+    for login in maintainers_logins:
+        user = authors[login]
+        maintainers.append(
+            {
+                "login": login,
+                "count": contributors[login], #+ question_commentors[login],
+                "avatarUrl": user.avatarUrl,
+                "twitterUsername": user.twitterUsername,
+                "url": user.url,
+            }
+        )
+
+    # min_count_expert = 10
+    # min_count_last_month = 3
+    min_score_contributor = 1
+    min_count_reviewer = 5
+    skip_users = maintainers_logins | bot_names | hidden_logins
+    # experts = get_top_users(
+    #     counter=question_commentors,
+    #     min_count=min_count_expert,
+    #     authors=authors,
+    #     skip_users=skip_users,
+    # )
+    # last_month_active = get_top_users(
+    #     counter=question_last_month_commentors,
+    #     min_count=min_count_last_month,
+    #     authors=authors,
+    #     skip_users=skip_users,
+    # )
+    top_recent_contributors = get_top_users(
+        counter=recent_contributor_scores,
+        min_count=min_score_contributor,
+        authors=authors,
+        skip_users=skip_users,
+    )
+    top_contributors = get_top_users(
+        counter=contributor_scores,
+        min_count=min_score_contributor,
+        authors=authors,
+        skip_users=skip_users,
+    )
+    top_reviewers = get_top_users(
+        counter=reviewers,
+        min_count=min_count_reviewer,
+        authors=authors,
+        skip_users=skip_users,
+    )
+
+    people = {
+        "maintainers": maintainers,
+        # "experts": experts,
+        # "last_month_active": last_month_active,
+        "top_recent_contributors": top_recent_contributors,
+        "top_contributors": top_contributors,
+        "top_reviewers": top_reviewers,
+    }
+    people_path = Path("./docs/data/people.yml")
+    people_old_content = people_path.read_text(encoding="utf-8")
+    new_people_content = yaml.dump(
+        people, sort_keys=False, width=200, allow_unicode=True
+    )
+    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")
+    logging.info("Setting up GitHub Actions git user")
+    subprocess.run(["git", "config", "user.name", "github-actions"], check=True)
+    subprocess.run(
+        ["git", "config", "user.email", "github-actions@github.com"], check=True
+    )
+    branch_name = "langchain/langchain-people"
+    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
+    )
+    logging.info("Committing updated file")
+    message = "👥 Update LangChain people data"
+    result = subprocess.run(["git", "commit", "-m", message], check=True)
+    logging.info("Pushing branch")
+    subprocess.run(["git", "push", "origin", branch_name, "-f"], check=True)
+    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")

.github/scripts/check_diff.py

@@ -48,3 +48,7 @@ if __name__ == "__main__":
             pass
     json_output = json.dumps(list(dirs_to_run))
     print(f"dirs-to-run={json_output}")  # noqa: T201
+
+    extended_test_dirs = [d for d in dirs_to_run if not d.startswith("libs/partners")]
+    json_output_extended = json.dumps(extended_test_dirs)
+    print(f"dirs-to-run-extended={json_output_extended}")  # noqa: T201

.github/workflows/_all_ci.yml

@@ -1,110 +0,0 @@
----
-name: langchain CI
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-  workflow_dispatch:
-    inputs:
-      working-directory:
-        required: true
-        type: choice
-        default: 'libs/langchain'
-        options:
-        - libs/langchain
-        - libs/core
-        - libs/experimental
-        - libs/community
-
-
-# If another push to the same PR or branch happens while this workflow is still running,
-# cancel the earlier run in favor of the next run.
-#
-# There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to cancel
-# pointless jobs early so that more useful jobs can run sooner.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ inputs.working-directory }}
-  cancel-in-progress: true
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  lint:
-    name: "-"
-    uses: ./.github/workflows/_lint.yml
-    with:
-      working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
-
-  test:
-    name: "-"
-    uses: ./.github/workflows/_test.yml
-    with:
-      working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
-
-  compile-integration-tests:
-    name: "-"
-    uses: ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
-
-  dependencies:
-    name: "-"
-    uses: ./.github/workflows/_dependencies.yml
-    with:
-      working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
-
-  extended-tests:
-    name: "make extended_tests #${{ matrix.python-version }}"
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
-    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: ${{ inputs.working-directory }}
-          cache-key: extended
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          echo "Running extended tests, installing dependencies with poetry..."
-          poetry install -E extended_testing --with test
-
-      - name: Run extended tests
-        run: make extended_tests
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(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

@@ -52,6 +52,7 @@ jobs:
       - name: Run integration tests
         shell: bash
         env:
+          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
@@ -66,6 +67,9 @@ jobs:
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
           PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
           PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
+          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
+          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
+          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: |
           make integration_tests
 

.github/workflows/_release.yml

@@ -166,6 +166,7 @@ jobs:
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
+          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
@@ -180,12 +181,16 @@ jobs:
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
+          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
           PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
           PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
+          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
+          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
+          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/check_diffs.yml

@@ -16,6 +16,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -31,14 +34,113 @@ jobs:
           python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
       dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }}
-  ci:
+      dirs-to-run-extended: ${{ steps.set-matrix.outputs.dirs-to-run-extended }}
+  lint:
     name: cd ${{ matrix.working-directory }}
     needs: [ build ]
     strategy:
       matrix:
         working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
-    uses: ./.github/workflows/_all_ci.yml
+    uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
+    secrets: inherit
 
+  test:
+    name: cd ${{ matrix.working-directory }}
+    needs: [ build ]
+    strategy:
+      matrix:
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
+    uses: ./.github/workflows/_test.yml
+    with:
+      working-directory: ${{ matrix.working-directory }}
+    secrets: inherit
 
+  compile-integration-tests:
+    name: cd ${{ matrix.working-directory }}
+    needs: [ build ]
+    strategy:
+      matrix:
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
+    uses: ./.github/workflows/_compile_integration_test.yml
+    with:
+      working-directory: ${{ matrix.working-directory }}
+    secrets: inherit
+
+  dependencies:
+    name: cd ${{ matrix.working-directory }}
+    needs: [ build ]
+    strategy:
+      matrix:
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
+    uses: ./.github/workflows/_dependencies.yml
+    with:
+      working-directory: ${{ matrix.working-directory }}
+    secrets: inherit
+
+  extended-tests:
+    name: "cd ${{ matrix.working-directory }} / make extended_tests #${{ matrix.python-version }}"
+    needs: [ build ]
+    if: ${{ needs.build.outputs.dirs-to-run-extended != '[]' }}
+    strategy:
+      matrix:
+        # note different variable for extended test dirs
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run-extended) }}
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    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: ${{ matrix.working-directory }}
+          cache-key: extended
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          echo "Running extended tests, installing dependencies with poetry..."
+          poetry install -E extended_testing --with test
+
+      - name: Run extended tests
+        run: make extended_tests
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(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'
+  ci_success:
+    name: "CI Success"
+    needs: [build, lint, test, compile-integration-tests, dependencies, extended-tests]
+    if: |
+      always()
+    runs-on: ubuntu-latest
+    env:
+      JOBS_JSON: ${{ toJSON(needs) }}
+      RESULTS_JSON: ${{ toJSON(needs.*.result) }}
+      EXIT_CODE: ${{!contains(needs.*.result, 'failure') && !contains(needs.*.result, 'cancelled') && '0' || '1'}}
+    steps:
+      - name: "CI Success"
+        run: |
+          echo $JOBS_JSON
+          echo $RESULTS_JSON
+          echo "Exiting with $EXIT_CODE"
+          exit $EXIT_CODE

.github/workflows/codespell.yml

@@ -32,6 +32,6 @@ jobs:
       - name: Codespell
         uses: codespell-project/actions-codespell@v2
         with:
-          skip: guide_imports.json
+          skip: guide_imports.json,*.ambr
           ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
           exclude_file: libs/community/langchain_community/llms/yuan2.py

.github/workflows/people.yml

@@ -0,0 +1,36 @@
+name: LangChain People
+
+on:
+  schedule:
+    - cron: "0 14 1 * *"
+  push:
+    branches: [jacob/people]
+  workflow_dispatch:
+    inputs:
+      debug_enabled:
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
+        required: false
+        default: 'false'
+
+jobs:
+  langchain-people:
+    if: github.repository_owner == 'langchain-ai'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Dump GitHub context
+        env:
+          GITHUB_CONTEXT: ${{ toJson(github) }}
+        run: echo "$GITHUB_CONTEXT"
+      - uses: actions/checkout@v4
+      # Ref: https://github.com/actions/runner/issues/2033
+      - name: Fix git safe.directory in container
+        run: mkdir -p /home/runner/work/_temp/_github_home && printf "[safe]\n\tdirectory = /github/workspace" > /home/runner/work/_temp/_github_home/.gitconfig
+      # Allow debugging with tmate
+      - name: Setup tmate session
+        uses: mxschmitt/action-tmate@v3
+        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled == 'true' }}
+        with:
+          limit-access-to-actor: true
+      - uses: ./.github/actions/people
+        with:
+          token: ${{ secrets.LANGCHAIN_PEOPLE_GITHUB_TOKEN }}

.gitignore

@@ -177,4 +177,6 @@ docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock
 _dist
-docs/docs/templates
+docs/docs/templates
+
+prof

Makefile

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

README.md

@@ -18,7 +18,7 @@ Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langc
 
 To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
 [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
+Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
 
 ## Quick Install
 

cookbook/advanced_rag_eval.ipynb

@@ -520,7 +520,7 @@
    "source": [
     "import re\n",
     "\n",
-    "from langchain.schema import Document\n",
+    "from langchain_core.documents import Document\n",
     "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",

cookbook/amazon_personalize_how_to.ipynb

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amazon Personalize\n",
+    "\n",
+    "[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata.\n",
+    "\n",
+    "This notebook goes through how to use Amazon Personalize Chain. You need a Amazon Personalize campaign_arn or a recommender_arn before you get started with the below notebook.\n",
+    "\n",
+    "Following is a [tutorial](https://github.com/aws-samples/retail-demo-store/blob/master/workshop/1-Personalization/Lab-1-Introduction-and-data-preparation.ipynb) to setup a campaign_arn/recommender_arn on Amazon Personalize. Once the campaign_arn/recommender_arn is setup, you can use it in the langchain ecosystem. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install boto3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Sample Use-cases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 [Use-case-1] Setup Amazon Personalize Client and retrieve recommendations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.recommenders import AmazonPersonalize\n",
+    "\n",
+    "recommender_arn = \"<insert_arn>\"\n",
+    "\n",
+    "client = AmazonPersonalize(\n",
+    "    credentials_profile_name=\"default\",\n",
+    "    region_name=\"us-west-2\",\n",
+    "    recommender_arn=recommender_arn,\n",
+    ")\n",
+    "client.get_recommendations(user_id=\"1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.2 [Use-case-2] Invoke Personalize Chain for summarizing results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms.bedrock import Bedrock\n",
+    "from langchain_experimental.recommenders import AmazonPersonalizeChain\n",
+    "\n",
+    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
+    "\n",
+    "# Create personalize chain\n",
+    "# Use return_direct=True if you do not want summary\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False\n",
+    ")\n",
+    "response = chain({\"user_id\": \"1\"})\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 [Use-Case-3] Invoke Amazon Personalize Chain using your own prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "RANDOM_PROMPT_QUERY = \"\"\"\n",
+    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
+    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
+    "    The movies to recommend and their information is contained in the <movie> tag. \n",
+    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
+    "    Put the email between <email> tags.\n",
+    "\n",
+    "    <movie>\n",
+    "    {result} \n",
+    "    </movie>\n",
+    "\n",
+    "    Assistant:\n",
+    "    \"\"\"\n",
+    "\n",
+    "RANDOM_PROMPT = PromptTemplate(input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY)\n",
+    "\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False, prompt_template=RANDOM_PROMPT\n",
+    ")\n",
+    "chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 [Use-case-4] Invoke Amazon Personalize in a Sequential Chain "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain, SequentialChain\n",
+    "\n",
+    "RANDOM_PROMPT_QUERY_2 = \"\"\"\n",
+    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
+    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
+    "    You want the email to impress the user, so make it appealing to them.\n",
+    "    The movies to recommend and their information is contained in the <movie> tag. \n",
+    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
+    "    Put the email between <email> tags.\n",
+    "\n",
+    "    <movie>\n",
+    "    {result}\n",
+    "    </movie>\n",
+    "\n",
+    "    Assistant:\n",
+    "    \"\"\"\n",
+    "\n",
+    "RANDOM_PROMPT_2 = PromptTemplate(\n",
+    "    input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY_2\n",
+    ")\n",
+    "personalize_chain_instance = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=True\n",
+    ")\n",
+    "random_chain_instance = LLMChain(llm=bedrock_llm, prompt=RANDOM_PROMPT_2)\n",
+    "overall_chain = SequentialChain(\n",
+    "    chains=[personalize_chain_instance, random_chain_instance],\n",
+    "    input_variables=[\"user_id\"],\n",
+    "    verbose=True,\n",
+    ")\n",
+    "overall_chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.5 [Use-case-5] Invoke Amazon Personalize and retrieve metadata "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "recommender_arn = \"<insert_arn>\"\n",
+    "metadata_column_names = [\n",
+    "    \"<insert metadataColumnName-1>\",\n",
+    "    \"<insert metadataColumnName-2>\",\n",
+    "]\n",
+    "metadataMap = {\"ITEMS\": metadata_column_names}\n",
+    "\n",
+    "client = AmazonPersonalize(\n",
+    "    credentials_profile_name=\"default\",\n",
+    "    region_name=\"us-west-2\",\n",
+    "    recommender_arn=recommender_arn,\n",
+    ")\n",
+    "client.get_recommendations(user_id=\"1\", metadataColumns=metadataMap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.6 [Use-Case 6] Invoke Personalize Chain with returned metadata for summarizing results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
+    "\n",
+    "# Create personalize chain\n",
+    "# Use return_direct=True if you do not want summary\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False\n",
+    ")\n",
+    "response = chain({\"user_id\": \"1\", \"metadata_columns\": metadataMap})\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "15e58ce194949b77a891bd4339ce3d86a9bd138e905926019517993f97db9e6c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

cookbook/apache_kafka_message_handling.ipynb

@@ -167,7 +167,7 @@
     "from langchain.llms import LlamaCpp\n",
     "from langchain.memory import ConversationTokenBufferMemory\n",
     "from langchain.prompts import PromptTemplate, load_prompt\n",
-    "from langchain.schema import SystemMessage\n",
+    "from langchain_core.messages import SystemMessage\n",
     "from langchain_experimental.chat_models import Llama2Chat\n",
     "from quixstreams import Application, State, message_key\n",
     "\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -42,9 +42,9 @@
     ")\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
     "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
     "from langchain_openai import OpenAI"
    ]
   },
@@ -114,8 +114,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
     "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings"
    ]
   },

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -67,9 +67,9 @@
     ")\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
     "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
     "from langchain_openai import OpenAI"
    ]
   },
@@ -138,8 +138,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
     "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings"
    ]
   },

cookbook/custom_agent_with_tool_retrieval.ipynb

@@ -40,8 +40,8 @@
     ")\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.utilities import SerpAPIWrapper\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
     "from langchain_openai import OpenAI"
    ]
   },
@@ -103,8 +103,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
     "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings"
    ]
   },

cookbook/custom_multi_action_agent.ipynb

@@ -72,7 +72,7 @@
    "source": [
     "from typing import Any, List, Tuple, Union\n",
     "\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
     "\n",
     "\n",
     "class FakeAgent(BaseMultiActionAgent):\n",

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -73,8 +73,9 @@
     "    AsyncCallbackManagerForRetrieverRun,\n",
     "    CallbackManagerForRetrieverRun,\n",
     ")\n",
-    "from langchain.schema import BaseRetriever, Document\n",
     "from langchain_community.utilities import GoogleSerperAPIWrapper\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_core.retrievers import BaseRetriever\n",
     "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },

cookbook/openai_functions_retrieval_qa.ipynb

@@ -358,7 +358,7 @@
     "\n",
     "from langchain.chains.openai_functions import create_qa_with_structure_chain\n",
     "from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from pydantic import BaseModel, Field"
    ]
   },

cookbook/rag_fusion.ipynb

@@ -19,7 +19,9 @@
    "source": [
     "## Setup\n",
     "\n",
-    "For this example, we will use Pinecone and some fake data"
+    "For this example, we will use Pinecone and some fake data. To configure Pinecone, set the following environment variable:\n",
+    "\n",
+    "- `PINECONE_API_KEY`: Your Pinecone API key"
    ]
   },
   {
@@ -29,11 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pinecone\n",
-    "from langchain_community.vectorstores import Pinecone\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "pinecone.init(api_key=\"...\", environment=\"...\")"
+    "from langchain_pinecone import PineconeVectorStore"
    ]
   },
   {
@@ -64,7 +63,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorstore = Pinecone.from_texts(\n",
+    "vectorstore = PineconeVectorStore.from_texts(\n",
     "    list(all_documents.values()), OpenAIEmbeddings(), index_name=\"rag-fusion\"\n",
     ")"
    ]
@@ -162,7 +161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorstore = Pinecone.from_existing_index(\"rag-fusion\", OpenAIEmbeddings())\n",
+    "vectorstore = PineconeVectorStore.from_existing_index(\"rag-fusion\", OpenAIEmbeddings())\n",
     "retriever = vectorstore.as_retriever()"
    ]
   },

cookbook/rag_with_quantized_embeddings.ipynb

@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6195da33-34c3-4ca2-943a-050b6dcbacbc",
+   "metadata": {},
+   "source": [
+    "# Embedding Documents using Optimized and Quantized Embedders\n",
+    "\n",
+    "In this tutorial, we will demo how to build a RAG pipeline, with the embedding for all documents done using Quantized Embedders.\n",
+    "\n",
+    "We will use a pipeline that will:\n",
+    "\n",
+    "* Create a document collection.\n",
+    "* Embed all documents using Quantized Embedders.\n",
+    "* Fetch relevant documents for our question.\n",
+    "* Run an LLM answer the question.\n",
+    "\n",
+    "For more information about optimized models, we refer to [optimum-intel](https://github.com/huggingface/optimum-intel.git) and [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n",
+    "\n",
+    "This tutorial is based on the [Langchain RAG tutorial here](https://towardsai.net/p/machine-learning/dense-x-retrieval-technique-in-langchain-and-llamaindex)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "26db2da5-3733-4a90-909e-6c11508ea140",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import langchain\n",
+    "import torch\n",
+    "from bs4 import BeautifulSoup as Soup\n",
+    "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.storage import InMemoryByteStore, LocalFileStore\n",
+    "\n",
+    "# For our example, we'll load docs from the web\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter  # noqa\n",
+    "from langchain_community.document_loaders.recursive_url_loader import (\n",
+    "    RecursiveUrlLoader,\n",
+    ")\n",
+    "\n",
+    "# noqa\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "\n",
+    "DOCSTORE_DIR = \".\"\n",
+    "DOCSTORE_ID_KEY = \"doc_id\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
+   "metadata": {},
+   "source": [
+    "Lets first load up this paper, and split into text chunks of size 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f4d8888-53a6-49f5-a198-da5c92419ca4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 1 documents\n",
+      "Split into 73 documents\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Could add more parsing here, as it's very raw.\n",
+    "loader = RecursiveUrlLoader(\n",
+    "    \"https://ar5iv.labs.arxiv.org/html/1706.03762\",\n",
+    "    max_depth=2,\n",
+    "    extractor=lambda x: Soup(x, \"html.parser\").text,\n",
+    ")\n",
+    "data = loader.load()\n",
+    "print(f\"Loaded {len(data)} documents\")\n",
+    "\n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)\n",
+    "print(f\"Split into {len(all_splits)} documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e90632-2ac2-49eb-80da-ffe9ac4a278d",
+   "metadata": {},
+   "source": [
+    "In order to embed our documents, we can use the ```QuantizedBiEncoderEmbeddings```, for efficient and fast embedding. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9a68a6f6-332d-481e-bbea-ad763155ea36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89af89b48c55409b9999b8e0387fab5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01ad1b6278194b53bf6a5a286a311864",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/45.9M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3bd1b88f7743c3b0322da3f021325c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "inc_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading configuration file inc_config.json from cache at \n",
+      "INCConfig {\n",
+      "  \"distillation\": {},\n",
+      "  \"neural_compressor_version\": \"2.4.1\",\n",
+      "  \"optimum_version\": \"1.16.2\",\n",
+      "  \"pruning\": {},\n",
+      "  \"quantization\": {\n",
+      "    \"dataset_num_samples\": 50,\n",
+      "    \"is_static\": true\n",
+      "  },\n",
+      "  \"save_onnx_model\": false,\n",
+      "  \"torch_version\": \"2.2.0\",\n",
+      "  \"transformers_version\": \"4.37.2\"\n",
+      "}\n",
+      "\n",
+      "Using `INCModel` to load a TorchScript model will be deprecated in v1.15.0, to load your model please use `IPEXModel` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7439315ebcb746f5be11fe30bc7693f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05265a3912254ce1ad43cc8086bcb0ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a48f4245c60744f28f37cd3a7a24d198",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "584a63cace934033b4ab22d3a178582a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from langchain_community.embeddings import QuantizedBiEncoderEmbeddings\n",
+    "from langchain_core.embeddings import Embeddings\n",
+    "\n",
+    "model_name = \"Intel/bge-small-en-v1.5-rag-int8-static\"\n",
+    "encode_kwargs = {\"normalize_embeddings\": True}  # set True to compute cosine similarity\n",
+    "\n",
+    "model_inc = QuantizedBiEncoderEmbeddings(\n",
+    "    model_name=model_name,\n",
+    "    encode_kwargs=encode_kwargs,\n",
+    "    query_instruction=\"Represent this sentence for searching relevant passages: \",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
+   "metadata": {},
+   "source": [
+    "With our embedder in place, lets define our retriever:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "18bc0a73-1a13-4b2f-96ac-05a5313343b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_multi_vector_retriever(\n",
+    "    docstore_id_key: str, collection_name: str, embedding_function: Embeddings\n",
+    "):\n",
+    "    \"\"\"Create the composed retriever object.\"\"\"\n",
+    "    vectorstore = Chroma(\n",
+    "        collection_name=collection_name,\n",
+    "        embedding_function=embedding_function,\n",
+    "    )\n",
+    "    store = InMemoryByteStore()\n",
+    "\n",
+    "    return MultiVectorRetriever(\n",
+    "        vectorstore=vectorstore,\n",
+    "        byte_store=store,\n",
+    "        id_key=docstore_id_key,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "retriever = get_multi_vector_retriever(DOCSTORE_ID_KEY, \"multi_vec_store\", model_inc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8484078e-1bf0-4080-a354-ef23823fd6dc",
+   "metadata": {},
+   "source": [
+    "Next, we divide each chunk into sub-docs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e12f48d4-6562-416b-8f28-342912e5756e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
+    "id_key = \"doc_id\"\n",
+    "doc_ids = [str(uuid.uuid4()) for _ in all_splits]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a268ef5f-91c2-4d8e-87f0-53db376e6a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sub_docs = []\n",
+    "for i, doc in enumerate(all_splits):\n",
+    "    _id = doc_ids[i]\n",
+    "    _sub_docs = child_text_splitter.split_documents([doc])\n",
+    "    for _doc in _sub_docs:\n",
+    "        _doc.metadata[id_key] = _id\n",
+    "    sub_docs.extend(_sub_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
+   "metadata": {},
+   "source": [
+    "Lets write our documents into our new store. This will use our embedder on each document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1af831ce-0eae-44bc-aca7-4d691063640b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 8/8 [00:00<00:00,  9.05it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "retriever.vectorstore.add_documents(sub_docs)\n",
+    "retriever.docstore.mset(list(zip(doc_ids, all_splits)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
+   "metadata": {},
+   "source": [
+    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "008c992f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbe70583ad964ae19582b72dab396784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
+    "\n",
+    "model_id = \"Intel/neural-chat-7b-v3-3\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n",
+    ")\n",
+    "\n",
+    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n",
+    "\n",
+    "hf = HuggingFacePipeline(pipeline=pipe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dd21fb2-0442-477d-aae2-9e7ee1d1d778",
+   "metadata": {},
+   "source": [
+    "Next, we will load up a prompt for answering questions using retrieved documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5e582509-caaf-4920-932c-4ce16162c789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cdfcba5-7ec7-4d0a-820e-4e200643a882",
+   "metadata": {},
+   "source": [
+    "We can now build our pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b74d8dfb-72bb-46da-9df9-0dc47a3ac791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "\n",
+    "rag_chain = {\"context\": retriever, \"question\": RunnablePassthrough()} | prompt | hf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
+   "metadata": {},
+   "source": [
+    "Excellent! lets ask it a question.\n",
+    "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f0a92c07-53da-4e1f-b880-ee83a36ee17d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] [1ms] Exiting Chain run with output:\n",
+      "\u001b[0m{\n",
+      "  \"output\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] [66ms] Exiting Chain run with output:\n",
+      "\u001b[0m[outputs]\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
+      "\u001b[0m[inputs]\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
+      "\u001b[0m{\n",
+      "  \"lc\": 1,\n",
+      "  \"type\": \"constructor\",\n",
+      "  \"id\": [\n",
+      "    \"langchain\",\n",
+      "    \"prompts\",\n",
+      "    \"chat\",\n",
+      "    \"ChatPromptValue\"\n",
+      "  ],\n",
+      "  \"kwargs\": {\n",
+      "    \"messages\": [\n",
+      "      {\n",
+      "        \"lc\": 1,\n",
+      "        \"type\": \"constructor\",\n",
+      "        \"id\": [\n",
+      "          \"langchain\",\n",
+      "          \"schema\",\n",
+      "          \"messages\",\n",
+      "          \"HumanMessage\"\n",
+      "        ],\n",
+      "        \"kwargs\": {\n",
+      "          \"content\": \"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\",\n",
+      "          \"additional_kwargs\": {}\n",
+      "        }\n",
+      "      }\n",
+      "    ]\n",
+      "  }\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] Entering LLM run with input:\n",
+      "\u001b[0m{\n",
+      "  \"prompts\": [\n",
+      "    \"Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\"\n",
+      "  ]\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] [4.34s] Exiting LLM run with output:\n",
+      "\u001b[0m{\n",
+      "  \"generations\": [\n",
+      "    [\n",
+      "      {\n",
+      "        \"text\": \" The first transduction model relying entirely on self-attention is the Transformer.\",\n",
+      "        \"generation_info\": null,\n",
+      "        \"type\": \"Generation\"\n",
+      "      }\n",
+      "    ]\n",
+      "  ],\n",
+      "  \"llm_output\": null,\n",
+      "  \"run\": null\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence] [4.41s] Exiting Chain run with output:\n",
+      "\u001b[0m{\n",
+      "  \"output\": \" The first transduction model relying entirely on self-attention is the Transformer.\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "langchain.verbose = True\n",
+    "langchain.debug = True\n",
+    "\n",
+    "llm_res = rag_chain.invoke(\n",
+    "    \"What is the first transduction model relying entirely on self-attention?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "023404a1-401a-46e1-8ab5-cafbc8593b04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The first transduction model relying entirely on self-attention is the Transformer.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eaefd01-254a-445d-a95f-37889c126e0e",
+   "metadata": {},
+   "source": [
+    "Based on the retrieved documents, the answer is indeed correct :)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

cookbook/sales_agent_with_context.ipynb

@@ -51,10 +51,10 @@
     "from langchain.chains.base import Chain\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.prompts.base import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain_community.llms import BaseLLM\n",
     "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
     "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n",
     "from pydantic import BaseModel, Field"
    ]

cookbook/wikibase_agent.ipynb

@@ -401,7 +401,7 @@
     ")\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish"
+    "from langchain_core.agents import AgentAction, AgentFinish"
    ]
   },
   {

docker/Makefile

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

docker/docker-compose.yml

@@ -15,3 +15,7 @@ services:
       - "6020:6379"
     volumes:
       - ./redis-volume:/data
+  graphdb:
+    image: graphdb
+    ports:
+      - "6021:7200"

docker/graphdb/Dockerfile

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

docker/graphdb/config.ttl

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

docker/graphdb/graphdb_create.sh

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

docs/api_reference/conf.py

@@ -49,7 +49,7 @@ class ExampleLinksDirective(SphinxDirective):
         class_or_func_name = self.arguments[0]
         links = imported_classes.get(class_or_func_name, {})
         list_node = nodes.bullet_list()
-        for doc_name, link in links.items():
+        for doc_name, link in sorted(links.items()):
             item_node = nodes.list_item()
             para_node = nodes.paragraph()
             link_node = nodes.reference()
@@ -114,8 +114,8 @@ autodoc_pydantic_field_signature_prefix = "param"
 autodoc_member_order = "groupwise"
 autoclass_content = "both"
 autodoc_typehints_format = "short"
+autodoc_typehints = "both"
 
-# autodoc_typehints = "description"
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["templates"]
 

docs/api_reference/create_api_rst.py

@@ -14,7 +14,6 @@ from pydantic import BaseModel
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
-
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
@@ -218,8 +217,8 @@ def _construct_doc(
 
     for module in namespaces:
         _members = members_by_namespace[module]
-        classes = _members["classes_"]
-        functions = _members["functions"]
+        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:`{package_namespace}.{module}`"
@@ -245,9 +244,6 @@ Classes
 """
 
             for class_ in sorted(classes, key=lambda c: c["qualified_name"]):
-                if not class_["is_public"]:
-                    continue
-
                 if class_["kind"] == "TypedDict":
                     template = "typeddict.rst"
                 elif class_["kind"] == "enum":
@@ -265,7 +261,7 @@ Classes
 """
 
         if functions:
-            _functions = [f["qualified_name"] for f in functions if f["is_public"]]
+            _functions = [f["qualified_name"] for f in functions]
             fstring = "\n    ".join(sorted(_functions))
             full_doc += f"""\
 Functions
@@ -323,30 +319,52 @@ def _package_dir(package_name: str = "langchain") -> Path:
 
 
 def _get_package_version(package_dir: Path) -> str:
-    with open(package_dir.parent / "pyproject.toml", "r") as f:
-        pyproject = toml.load(f)
+    """Return the version of the package."""
+    try:
+        with open(package_dir.parent / "pyproject.toml", "r") as f:
+            pyproject = toml.load(f)
+    except FileNotFoundError as e:
+        print(
+            f"pyproject.toml not found in {package_dir.parent}.\n"
+            "You are either attempting to build a directory which is not a package or "
+            "the package is missing a pyproject.toml file which should be added."
+            "Aborting the build."
+        )
+        exit(1)
     return pyproject["tool"]["poetry"]["version"]
 
 
-def _out_file_path(package_name: str = "langchain") -> Path:
+def _out_file_path(package_name: str) -> Path:
     """Return the path to the file containing the documentation."""
     return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
-def _doc_first_line(package_name: str = "langchain") -> str:
+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() -> None:
     """Generate the api_reference.rst file for each package."""
+    print("Starting to build API reference files.")
     for dir in os.listdir(ROOT_DIR / "libs"):
+        # Skip any hidden directories
+        # Some of these could be present by mistake in the code base
+        # e.g., .pytest_cache from running tests from the wrong location.
+        if dir.startswith("."):
+            print("Skipping dir:", dir)
+            continue
+
         if dir in ("cli", "partners"):
             continue
         else:
+            print("Building package:", dir)
             _build_rst_file(package_name=dir)
-    for dir in os.listdir(ROOT_DIR / "libs" / "partners"):
+    partner_packages = os.listdir(ROOT_DIR / "libs" / "partners")
+    print("Building partner packages:", partner_packages)
+    for dir in partner_packages:
         _build_rst_file(package_name=dir)
+    print("API reference files built.")
 
 
 if __name__ == "__main__":

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

@@ -5,7 +5,7 @@
   <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" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script>
   <script type="text/javascript">
     $(document).ready(function() {
       if (!Search.out) {

docs/docs/contributing/documentation.mdx

@@ -3,24 +3,68 @@ sidebar_position: 3
 ---
 # Contribute Documentation
 
-The docs directory contains Documentation and API Reference.
+LangChain documentation consists of two components:
 
-Documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
+1. Main Documentation: Hosted at [python.langchain.com](https://python.langchain.com/),
+this comprehensive resource serves as the primary user-facing documentation.
+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://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.
 
-API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code and are hosted by [Read the Docs](https://readthedocs.org/).
-For that reason, we ask that you add good documentation to all classes and methods.
+The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
 
-Similar to linting, we recognize documentation 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.
+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/).
 
-## Build Documentation Locally
+We appreciate all contributions to the documentation, whether it be fixing a typo,
+adding a new tutorial or example and whether it be in the main documentation or the API Reference.
+
+Similar to linting, we recognize documentation 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.
+
+## 📜 Main Documentation
+
+The content for the main documentation is located in the `/docs` directory of the monorepo.
+
+The documentation is written using a combination of ipython notebooks (`.ipynb` files)
+and markdown (`.mdx` files). The notebooks are converted to markdown
+using [Quarto](https://quarto.org) and then built using [Docusaurus 2](https://docusaurus.io/).
+
+Feel free to make contributions to the main documentation! 🥰
+
+After modifying the documentation:
+
+1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
+2. Optionally build the documentation locally to verify that the changes look good.
+3. Make a pull request with the changes.
+4. You can preview and verify that the changes are what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page. This will take you to a preview of the documentation changes.
+
+## ⚒️ Linting and Building Documentation Locally
+
+After writing up the documentation, you may want to lint and build the documentation
+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.
-- `poetry install` from the monorepo root
+- [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
+poetry install --with lint,docs --no-root
+````
 
 ### Building
 
+The code that builds the documentation is located in the `/docs` directory of the monorepo.
+
 In the following commands, the prefix `api_` indicates that those are operations for the API Reference.
 
 Before building the documentation, it is always a good idea to clean the build directory:
@@ -46,10 +90,9 @@ make api_docs_linkcheck
 
 ### Linting and Formatting
 
-The docs are linted from the monorepo root. To lint the docs, run the following from there:
+The Main Documentation is linted from the **monorepo root**. To lint the main documentation, run the following from there:
 
 ```bash
-poetry install --with lint,typing
 make lint
 ```
 
@@ -57,9 +100,73 @@ If you have formatting-related errors, you can fix them automatically with:
 
 ```bash
 make format
-``` 
+```
 
-## Verify Documentation changes
+## ⌨️ In-code Documentation
+
+The in-code documentation 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/).
+
+For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
+
+We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
+
+Here is an example of a well-documented function:
+
+```python
+
+def my_function(arg1: int, arg2: str) -> float:
+    """This is a short description of the function. (It should be a single sentence.)
+
+    This is a longer description of the function. It should explain what
+    the function does, what the arguments are, and what the return value is.
+    It should wrap at 88 characters.
+
+    Examples:
+        This is a section for examples of how to use the function.
+
+        .. code-block:: python
+
+            my_function(1, "hello")
+
+    Args:
+        arg1: This is a description of arg1. We do not need to specify the type since
+            it is already specified in the function signature.
+        arg2: This is a description of arg2.
+
+    Returns:
+        This is a description of the return value.
+    """
+    return 3.14
+```
+
+### Linting and Formatting
+
+The in-code documentation is linted from the directories belonging to the packages
+being documented.
+
+For example, if you're working on the `langchain-community` package, you would change
+the working directory to the `langchain-community` directory:
+
+```bash
+cd [root]/libs/langchain-community
+```
+
+Set up a virtual environment for the package if you haven't done so already.
+
+Install the dependencies for the package.
+
+```bash
+poetry install --with lint
+```
+
+Then you can run the following commands to lint and format the in-code documentation:
+
+```bash
+make format
+make lint
+```
+
+## Verify Documentation Changes
 
 After pushing documentation changes to the repository, you can preview and verify that the changes are
 what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page.

docs/docs/contributing/index.mdx

@@ -15,8 +15,9 @@ There are many ways to contribute to LangChain. Here are some common ways people
 - [**Documentation**](./documentation.mdx): Help improve our docs, including this one!
 - [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
+- [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
 
-### 🚩GitHub Issues
+### 🚩 GitHub Issues
 
 Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
 
@@ -31,7 +32,13 @@ We will try to keep these issues as up-to-date as possible, though
 with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
-### 🙋Getting Help
+### 💭 GitHub Discussions
+
+We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
+
+If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
+
+### 🙋 Getting Help
 
 Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
 contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is

docs/docs/contributing/repo_structure.mdx

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

docs/docs/expression_language/cookbook/agent.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Agents\n",
     "\n",
-    "You can pass a Runnable into an agent."
+    "You can pass a Runnable into an agent. Make sure you have `langchainhub` installed: `pip install langchainhub`"
    ]
   },
   {
@@ -98,7 +98,7 @@
    "source": [
     "Building an agent from a runnable usually involves a few things:\n",
     "\n",
-    "1. Data processing for the intermediate steps. These need to represented in a way that the language model can recognize them. This should be pretty tightly coupled to the instructions in the prompt\n",
+    "1. Data processing for the intermediate steps. These need to be represented in a way that the language model can recognize them. This should be pretty tightly coupled to the instructions in the prompt\n",
     "\n",
     "2. The prompt itself\n",
     "\n",

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -47,7 +47,7 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.schema import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -169,8 +169,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import format_document\n",
     "from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string\n",
+    "from langchain_core.prompts import format_document\n",
     "from langchain_core.runnables import RunnableParallel"
    ]
   },

docs/docs/expression_language/how_to/binding.ipynb

@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
     "from langchain_openai import ChatOpenAI"

docs/docs/expression_language/how_to/message_history.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Add message history (memory)\n",
     "\n",
-    "The `RunnableWithMessageHistory` let us add message history to certain types of chains.\n",
+    "The `RunnableWithMessageHistory` lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it.\n",
     "\n",
     "Specifically, it can be used for any Runnable that takes as input one of\n",
     "\n",
@@ -21,7 +21,379 @@
     "* a sequence of `BaseMessage`\n",
     "* a dict with a key that contains a sequence of `BaseMessage`\n",
     "\n",
-    "Let's take a look at some examples to see how it works."
+    "Let's take a look at some examples to see how it works. First we construct a runnable (which here accepts a dict as input and returns a message as output):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ed413b4-33a1-48ee-89b0-2d4917ec101a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You're an assistant who's good at {ability}. Respond in 20 words or fewer\",\n",
+    "        ),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "runnable = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd175e1-c7b8-4929-a57e-3331865fe7aa",
+   "metadata": {},
+   "source": [
+    "To manage the message history, we will need:\n",
+    "1. This runnable;\n",
+    "2. A callable that returns an instance of `BaseChatMessageHistory`.\n",
+    "\n",
+    "Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using Redis and other providers. Here we demonstrate using an in-memory `ChatMessageHistory` as well as more persistent storage using `RedisChatMessageHistory`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d83adad-9672-496d-9f25-5747e7b8c8bb",
+   "metadata": {},
+   "source": [
+    "## In-memory\n",
+    "\n",
+    "Below we show a simple example in which the chat history lives in memory, in this case via a global Python dict.\n",
+    "\n",
+    "We construct a callable `get_session_history` that references this dict to return an instance of `ChatMessageHistory`. The arguments to the callable can be specified by passing a configuration to the `RunnableWithMessageHistory` at runtime. By default, the configuration parameter is expected to be a single string `session_id`. This can be adjusted via the `history_factory_config` kwarg.\n",
+    "\n",
+    "Using the single-parameter default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "54348d02-d8ee-440c-bbf9-41bc0fbbc46c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
+    "from langchain_core.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "\n",
+    "store = {}\n",
+    "\n",
+    "\n",
+    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
+    "    if session_id not in store:\n",
+    "        store[session_id] = ChatMessageHistory()\n",
+    "    return store[session_id]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01acb505-3fd3-4ab4-9f04-5ea07e81542e",
+   "metadata": {},
+   "source": [
+    "Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n",
+    "\n",
+    "When invoking this new runnable, we specify the corresponding chat history via a configuration parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "01384412-f08e-4634-9edb-3f46f475b582",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Cosine is a trigonometric function that calculates the ratio of the adjacent side to the hypotenuse of a right triangle.')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "954688a2-9a3f-47ee-a9e8-fa0c83e69477",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Cosine is a mathematical function used to calculate the length of a side in a right triangle.')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Remembers\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39350d7c-2641-4744-bc2a-fd6a57c4ea90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='I can help with math problems. What do you need assistance with?')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# New session_id --> does not remember.\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"def234\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d29497be-3366-408d-bbb9-d4a8bf4ef37c",
+   "metadata": {},
+   "source": [
+    "The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c89daee-deff-4fdf-86a3-178f7d8ef536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import ConfigurableFieldSpec\n",
+    "\n",
+    "store = {}\n",
+    "\n",
+    "\n",
+    "def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:\n",
+    "    if (user_id, conversation_id) not in store:\n",
+    "        store[(user_id, conversation_id)] = ChatMessageHistory()\n",
+    "    return store[(user_id, conversation_id)]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input\",\n",
+    "    history_messages_key=\"history\",\n",
+    "    history_factory_config=[\n",
+    "        ConfigurableFieldSpec(\n",
+    "            id=\"user_id\",\n",
+    "            annotation=str,\n",
+    "            name=\"User ID\",\n",
+    "            description=\"Unique identifier for the user.\",\n",
+    "            default=\"\",\n",
+    "            is_shared=True,\n",
+    "        ),\n",
+    "        ConfigurableFieldSpec(\n",
+    "            id=\"conversation_id\",\n",
+    "            annotation=str,\n",
+    "            name=\"Conversation ID\",\n",
+    "            description=\"Unique identifier for the conversation.\",\n",
+    "            default=\"\",\n",
+    "            is_shared=True,\n",
+    "        ),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65c5622e-09b8-4f2f-8c8a-2dab0fd040fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"Hello\"},\n",
+    "    config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18f1a459-3f88-4ee6-8542-76a907070dd6",
+   "metadata": {},
+   "source": [
+    "### Examples with runnables of different signatures\n",
+    "\n",
+    "The above runnable takes a dict as input and returns a BaseMessage. Below we show some alternatives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48eae1bf-b59d-4a61-8e62-b6dbf667e866",
+   "metadata": {},
+   "source": [
+    "#### Messages input, dict output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "17733d4f-3a32-4055-9d44-5d58b9446a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=\"Simone de Beauvoir believed in the existence of free will. She argued that individuals have the ability to make choices and determine their own actions, even in the face of social and cultural constraints. She rejected the idea that individuals are purely products of their environment or predetermined by biology or destiny. Instead, she emphasized the importance of personal responsibility and the need for individuals to actively engage in creating their own lives and defining their own existence. De Beauvoir believed that freedom and agency come from recognizing one's own freedom and actively exercising it in the pursuit of personal and collective liberation.\")}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.runnables import RunnableParallel\n",
+    "\n",
+    "chain = RunnableParallel({\"output_message\": ChatOpenAI()})\n",
+    "\n",
+    "\n",
+    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
+    "    if session_id not in store:\n",
+    "        store[session_id] = ChatMessageHistory()\n",
+    "    return store[session_id]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    get_session_history,\n",
+    "    output_messages_key=\"output_message\",\n",
+    ")\n",
+    "\n",
+    "with_message_history.invoke(\n",
+    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "efb57ef5-91f9-426b-84b9-b77f071a9dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content='Simone de Beauvoir\\'s views on free will were closely aligned with those of her contemporary and partner Jean-Paul Sartre. Both de Beauvoir and Sartre were existentialist philosophers who emphasized the importance of individual freedom and the rejection of determinism. They believed that human beings have the capacity to transcend their circumstances and create their own meaning and values.\\n\\nSartre, in his famous work \"Being and Nothingness,\" argued that human beings are condemned to be free, meaning that we are burdened with the responsibility of making choices and defining ourselves in a world that lacks inherent meaning. Like de Beauvoir, Sartre believed that individuals have the ability to exercise their freedom and make choices in the face of external and internal constraints.\\n\\nWhile there may be some nuanced differences in their philosophical writings, overall, de Beauvoir and Sartre shared a similar belief in the existence of free will and the importance of individual agency in shaping one\\'s own life.')}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a39eac5f-a9d8-4729-be06-5e7faf0c424d",
+   "metadata": {},
+   "source": [
+    "#### Messages input, messages output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e45bcd95-e31f-4a9a-967a-78f96e8da881",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RunnableWithMessageHistory(\n",
+    "    ChatOpenAI(),\n",
+    "    get_session_history,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04daa921-a2d1-40f9-8cd1-ae4e9a4163a7",
+   "metadata": {},
+   "source": [
+    "#### Dict with single key for all messages input, messages output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27157f15-9fb0-4167-9870-f4d7f234b3cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "RunnableWithMessageHistory(\n",
+    "    itemgetter(\"input_messages\") | ChatOpenAI(),\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input_messages\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418ca7af-9ed9-478c-8bca-cba0de2ca61e",
+   "metadata": {},
+   "source": [
+    "## Persistent storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76799a13-d99a-4c4f-91f2-db699e40b8df",
+   "metadata": {},
+   "source": [
+    "In many cases it is preferable to persist conversation histories. `RunnableWithMessageHistory` is agnostic as to how the `get_session_history` callable retrieves its chat message histories. See [here](https://github.com/langchain-ai/langserve/blob/main/examples/chat_with_persistence_and_user/server.py) for an example using a local filesystem. Below we demonstrate how one could use Redis. Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers."
    ]
   },
   {
@@ -29,9 +401,9 @@
    "id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd",
    "metadata": {},
    "source": [
-    "## Setup\n",
+    "### Setup\n",
     "\n",
-    "We'll use Redis to store our chat message histories and Anthropic's claude-2 model so we'll need to install the following dependencies:"
+    "We'll need to install Redis if it's not installed already:"
    ]
   },
   {
@@ -41,28 +413,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain redis anthropic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93776323-d6b8-4912-bb6a-867c5e655f46",
-   "metadata": {},
-   "source": [
-    "Set your [Anthropic API  key](https://console.anthropic.com/):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7f56f69-d2f1-4a21-990c-b5551eb012fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
+    "%pip install --upgrade --quiet redis"
    ]
   },
   {
@@ -78,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68",
    "metadata": {},
    "outputs": [],
@@ -110,77 +461,32 @@
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "1a5a632e-ba9e-4488-b586-640ad5494f62",
-   "metadata": {},
-   "source": [
-    "## Example: Dict input, message output\n",
-    "\n",
-    "Let's create a simple chain that takes a dict as input and returns a BaseMessage.\n",
-    "\n",
-    "In this case the `\"question\"` key in the input represents our input message, and the `\"history\"` key is where our historical messages will be injected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2a150d6f-8878-4950-8634-a608c5faad56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional\n",
-    "\n",
-    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
-    "from langchain_community.chat_models import ChatAnthropic\n",
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3185edba-4eb6-4b32-80c6-577c0d19af97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You're an assistant who's good at {ability}\"),\n",
-    "        MessagesPlaceholder(variable_name=\"history\"),\n",
-    "        (\"human\", \"{question}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | ChatAnthropic(model=\"claude-2\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef",
    "metadata": {},
    "source": [
-    "### Adding message history\n",
-    "\n",
-    "To add message history to our original chain we wrap it in the `RunnableWithMessageHistory` class.\n",
-    "\n",
-    "Crucially, we also need to  define a method that takes a session_id string and based on it returns a `BaseChatMessageHistory`. Given the same input, this method should return an equivalent output.\n",
-    "\n",
-    "In this case we'll also want to specify `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to)."
+    "Updating the message history implementation just requires us to define a new callable, this time returning an instance of `RedisChatMessageHistory`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "ca7c64d8-e138-4ef8-9734-f82076c47d80",
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain_with_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    input_messages_key=\"question\",\n",
+    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
+    "\n",
+    "\n",
+    "def get_message_history(session_id: str) -> RedisChatMessageHistory:\n",
+    "    return RedisChatMessageHistory(session_id, url=REDIS_URL)\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_message_history,\n",
+    "    input_messages_key=\"input\",\n",
     "    history_messages_key=\"history\",\n",
     ")"
    ]
@@ -190,60 +496,53 @@
    "id": "37eefdec-9901-4650-b64c-d3c097ed5f4d",
    "metadata": {},
    "source": [
-    "## Invoking with config\n",
-    "\n",
-    "Whenever we call our chain with message history, we need to include a config that contains the `session_id`\n",
-    "```python\n",
-    "config={\"configurable\": {\"session_id\": \"<SESSION_ID>\"}}\n",
-    "```\n",
-    "\n",
-    "Given the same configuration, our chain should be pulling from the same chat message history."
+    "We can invoke as before:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "a85bcc22-ca4c-4ad5-9440-f94be7318f3e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' Cosine is one of the basic trigonometric functions in mathematics. It is defined as the ratio of the adjacent side to the hypotenuse in a right triangle.\\n\\nSome key properties and facts about cosine:\\n\\n- It is denoted by cos(θ), where θ is the angle in a right triangle. \\n\\n- The cosine of an acute angle is always positive. For angles greater than 90 degrees, cosine can be negative.\\n\\n- Cosine is one of the three main trig functions along with sine and tangent.\\n\\n- The cosine of 0 degrees is 1. As the angle increases towards 90 degrees, the cosine value decreases towards 0.\\n\\n- The range of values for cosine is -1 to 1.\\n\\n- The cosine function maps angles in a circle to the x-coordinate on the unit circle.\\n\\n- Cosine is used to find adjacent side lengths in right triangles, and has many other applications in mathematics, physics, engineering and more.\\n\\n- Key cosine identities include: cos(A+B) = cosAcosB − sinAsinB and cos(2A) = cos^2(A) − sin^2(A)\\n\\nSo in summary, cosine is a fundamental trig')"
+       "AIMessage(content='Cosine is a trigonometric function that represents the ratio of the adjacent side to the hypotenuse in a right triangle.')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_history.invoke(\n",
-    "    {\"ability\": \"math\", \"question\": \"What does cosine mean?\"},\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "ab29abd3-751f-41ce-a1b0-53f6b565e79d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' The inverse of the cosine function is called the arccosine or inverse cosine, often denoted as cos-1(x) or arccos(x).\\n\\nThe key properties and facts about arccosine:\\n\\n- It is defined as the angle θ between 0 and π radians whose cosine is x. So arccos(x) = θ such that cos(θ) = x.\\n\\n- The range of arccosine is 0 to π radians (0 to 180 degrees).\\n\\n- The domain of arccosine is -1 to 1. \\n\\n- arccos(cos(θ)) = θ for values of θ from 0 to π radians.\\n\\n- arccos(x) is the angle in a right triangle whose adjacent side is x and hypotenuse is 1.\\n\\n- arccos(0) = 90 degrees. As x increases from 0 to 1, arccos(x) decreases from 90 to 0 degrees.\\n\\n- arccos(1) = 0 degrees. arccos(-1) = 180 degrees.\\n\\n- The graph of y = arccos(x) is part of the unit circle, restricted to x')"
+       "AIMessage(content='The inverse of cosine is the arccosine function, denoted as acos or cos^-1, which gives the angle corresponding to a given cosine value.')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_history.invoke(\n",
-    "    {\"ability\": \"math\", \"question\": \"What's its inverse\"},\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What's its inverse\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
@@ -255,7 +554,7 @@
    "source": [
     ":::tip\n",
     "\n",
-    "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
+    "[Langsmith trace](https://smith.langchain.com/public/bd73e122-6ec1-48b2-82df-e6483dc9cb63/r)\n",
     "\n",
     ":::"
    ]
@@ -267,124 +566,13 @@
    "source": [
     "Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "028cf151-6cd5-4533-b3cf-c8d735554647",
-   "metadata": {},
-   "source": [
-    "## Example: messages input, dict output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0bb446b5-6251-45fe-a92a-4c6171473c53",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content=' Here is a summary of Simone de Beauvoir\\'s views on free will:\\n\\n- De Beauvoir was an existentialist philosopher and believed strongly in the concept of free will. She rejected the idea that human nature or instincts determine behavior.\\n\\n- Instead, de Beauvoir argued that human beings define their own essence or nature through their actions and choices. As she famously wrote, \"One is not born, but rather becomes, a woman.\"\\n\\n- De Beauvoir believed that while individuals are situated in certain cultural contexts and social conditions, they still have agency and the ability to transcend these situations. Freedom comes from choosing one\\'s attitude toward these constraints.\\n\\n- She emphasized the radical freedom and responsibility of the individual. We are \"condemned to be free\" because we cannot escape making choices and taking responsibility for our choices. \\n\\n- De Beauvoir felt that many people evade their freedom and responsibility by adopting rigid mindsets, ideologies, or conforming uncritically to social roles.\\n\\n- She advocated for the recognition of ambiguity in the human condition and warned against the quest for absolute rules that deny freedom and responsibility. Authentic living involves embracing ambiguity.\\n\\nIn summary, de Beauvoir promoted an existential ethics')}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.runnables import RunnableParallel\n",
-    "\n",
-    "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n",
-    "chain_with_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    output_messages_key=\"output_message\",\n",
-    ")\n",
-    "\n",
-    "chain_with_history.invoke(\n",
-    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "601ce3ff-aea8-424d-8e54-fd614256af4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content=\" There are many similarities between Simone de Beauvoir's views on free will and those of Jean-Paul Sartre, though some key differences emerge as well:\\n\\nSimilarities with Sartre:\\n\\n- Both were existentialist thinkers who rejected determinism and emphasized human freedom and responsibility.\\n\\n- They agreed that existence precedes essence - there is no predefined human nature that determines who we are.\\n\\n- Individuals must define themselves through their choices and actions. This leads to anxiety but also freedom.\\n\\n- The human condition is characterized by ambiguity and uncertainty, rather than fixed meanings/values.\\n\\n- Both felt that most people evade their freedom through self-deception, conformity, or adopting collective identities/values uncritically.\\n\\nDifferences from Sartre: \\n\\n- Sartre placed more emphasis on the burden and anguish of radical freedom. De Beauvoir focused more on its positive potential.\\n\\n- De Beauvoir critiqued Sartre's premise that human relations are necessarily conflictual. She saw more potential for mutual recognition.\\n\\n- Sartre saw the Other's gaze as a threat to freedom. De Beauvoir put more stress on how the Other's gaze can confirm\")}"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_history.invoke(\n",
-    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611",
-   "metadata": {},
-   "source": [
-    ":::tip\n",
-    "\n",
-    "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
-    "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1724292c-01c6-44bb-83e8-9cdb6bf01483",
-   "metadata": {},
-   "source": [
-    "## More examples\n",
-    "\n",
-    "We could also do any of the below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd89240b-5a25-48f8-9568-5c1127f9ffad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter\n",
-    "\n",
-    "# messages in, messages out\n",
-    "RunnableWithMessageHistory(\n",
-    "    ChatAnthropic(model=\"claude-2\"),\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    ")\n",
-    "\n",
-    "# dict with single key for all messages in, messages out\n",
-    "RunnableWithMessageHistory(\n",
-    "    itemgetter(\"input_messages\") | ChatAnthropic(model=\"claude-2\"),\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    input_messages_key=\"input_messages\",\n",
-    ")"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -396,7 +584,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/streaming.ipynb

@@ -68,7 +68,7 @@
    "source": [
     "# Showing the example using anthropic, but you can use\n",
     "# your favorite chat model!\n",
-    "from langchain.chat_models import ChatAnthropic\n",
+    "from langchain_community.chat_models import ChatAnthropic\n",
     "\n",
     "model = ChatAnthropic()\n",
     "\n",

docs/docs/get_started/quickstart.mdx

@@ -58,7 +58,7 @@ LangChain enables building application that connect external sources of data and
 In this quickstart, we will walk through a few different ways of doing that.
 We will start with a simple LLM chain, which just relies on information in the prompt template to respond.
 Next, we will build a retrieval chain, which fetches data from a separate database and passes that into the prompt template.
-We will then add in chat history, to create a conversation retrieval chain. This allows you interact in a chat manner with this LLM, so it remembers previous questions.
+We will then add in chat history, to create a conversation retrieval chain. This allows you to interact in a chat manner with this LLM, so it remembers previous questions.
 Finally, we will build an agent - which utilizes an LLM to determine whether or not it needs to fetch data to answer questions.
 We will cover these at a high level, but there are lot of details to all of these!
 We will link to relevant docs.
@@ -193,7 +193,7 @@ After that, we can import and use WebBaseLoader.
 
 ```python
 from langchain_community.document_loaders import WebBaseLoader
-loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
+loader = WebBaseLoader("https://docs.smith.langchain.com")
 
 docs = loader.load()
 ```
@@ -374,7 +374,7 @@ The final thing we will create is an agent - where the LLM decides what steps to
 **NOTE: for this example we will only show how to create an agent using OpenAI models, as local models are not reliable enough yet.**
 
 One of the first things to do when building an agent is to decide what tools it should have access to.
-For this example, we will give the agent access two tools:
+For this example, we will give the agent access to two tools:
 
 1. The retriever we just created. This will let it easily answer questions about LangSmith
 2. A search tool. This will let it easily answer questions that require up to date information.

docs/docs/guides/evaluation/trajectory/custom.ipynb

@@ -35,7 +35,7 @@
     "\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.evaluation import AgentTrajectoryEvaluator\n",
-    "from langchain.schema import AgentAction\n",
+    "from langchain_core.agents import AgentAction\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -90,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "documents = [Document(page_content=document_content)]"
    ]
@@ -879,7 +879,7 @@
    "outputs": [],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain.schema import format_document\n",
+    "from langchain_core.prompts import format_document\n",
     "\n",
     "DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
     "\n",

docs/docs/integrations/callbacks/labelstudio.ipynb

@@ -242,7 +242,7 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import LabelStudioCallbackHandler\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "chat_llm = ChatOpenAI(\n",

docs/docs/integrations/callbacks/llmonitor.md

@@ -53,7 +53,7 @@ Example:
 
 ```python
 from langchain_openai import ChatOpenAI
-from langchain.schema import SystemMessage, HumanMessage
+from langchain_core.messages import SystemMessage, HumanMessage
 from langchain.agents import OpenAIFunctionsAgent, AgentExecutor, tool
 from langchain.callbacks import LLMonitorCallbackHandler
 

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -267,7 +267,7 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import TrubricsCallbackHandler\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },

docs/docs/integrations/chat/ai21.ipynb

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

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -83,7 +83,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage"
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/anyscale.ipynb

@@ -109,7 +109,7 @@
    "source": [
     "import asyncio\n",
     "\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a helpful AI that shares everything you know.\"),\n",

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -31,7 +31,7 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.schema import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from langchain_openai import AzureChatOpenAI"
    ]
   },

docs/docs/integrations/chat/azureml_chat_endpoint.ipynb

@@ -74,11 +74,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models.azureml_endpoint import (\n",
     "    AzureMLEndpointApiType,\n",
     "    LlamaChatContentFormatter,\n",
-    ")"
+    ")\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {
@@ -105,8 +105,8 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models.azureml_endpoint import LlamaContentFormatter\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "chat = AzureMLChatOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/score\",\n",

docs/docs/integrations/chat/baichuan.ipynb

@@ -29,8 +29,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ChatBaichuan"
+    "from langchain_community.chat_models import ChatBaichuan\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/bedrock.ipynb

@@ -47,8 +47,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import BedrockChat"
+    "from langchain_community.chat_models import BedrockChat\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/deepinfra.ipynb

@@ -68,8 +68,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatDeepInfra\n",
-    "from langchain.schema import HumanMessage"
+    "from langchain_community.chat_models import ChatDeepInfra\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {
@@ -216,7 +216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/ernie.ipynb

@@ -76,8 +76,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models import ErnieBotChat\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "chat = ErnieBotChat(\n",
     "    ernie_client_id=\"YOUR_CLIENT_ID\", ernie_client_secret=\"YOUR_CLIENT_SECRET\"\n",

docs/docs/integrations/chat/everlyai.ipynb

@@ -73,8 +73,8 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage, SystemMessage\n",
     "from langchain_community.chat_models import ChatEverlyAI\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a helpful AI that shares everything you know.\"),\n",
@@ -127,8 +127,8 @@
    ],
    "source": [
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
     "from langchain_community.chat_models import ChatEverlyAI\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",
@@ -185,8 +185,8 @@
    ],
    "source": [
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
     "from langchain_community.chat_models import ChatEverlyAI\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",

docs/docs/integrations/chat/fireworks.ipynb

@@ -37,8 +37,8 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain_community.chat_models.fireworks import ChatFireworks"
+    "from langchain_community.chat_models.fireworks import ChatFireworks\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {

docs/docs/integrations/chat/gigachat.ipynb

@@ -75,7 +75,7 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(\n",

docs/docs/integrations/chat/gpt_router.ipynb

@@ -70,9 +70,9 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models import GPTRouter\n",
-    "from langchain_community.chat_models.gpt_router import GPTRouterModel"
+    "from langchain_community.chat_models.gpt_router import GPTRouterModel\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/groq.ipynb

@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Groq\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Groq\n",
+    "\n",
+    "Install the langchain-groq package if not already installed:\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-groq\n",
+    "```\n",
+    "\n",
+    "Request an [API key](https://wow.groq.com) and set it as an environment variable:\n",
+    "\n",
+    "```bash\n",
+    "export GROQ_API_KEY=<YOUR API KEY>\n",
+    "```\n",
+    "\n",
+    "Alternatively, you may configure the API key when you initialize ChatGroq."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the ChatGroq class and initialize it with a model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_groq import ChatGroq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatGroq(temperature=0, model_name=\"mixtral-8x7b-32768\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can view the available models [here](https://console.groq.com/docs/models).\n",
+    "\n",
+    "If you do not want to set your API key in the environment, you can pass it directly to the client:\n",
+    "```python\n",
+    "chat = ChatGroq(temperature=0, groq_api_key=\"YOUR_API_KEY\", model_name=\"mixtral-8x7b-32768\")\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a prompt and invoke ChatGroq to create completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Low Latency Large Language Models (LLMs) are a type of artificial intelligence model that can understand and generate human-like text. The term \"low latency\" refers to the model\\'s ability to process and respond to inputs quickly, with minimal delay.\\n\\nThe importance of low latency in LLMs can be explained through the following points:\\n\\n1. Improved user experience: In real-time applications such as chatbots, virtual assistants, and interactive games, users expect quick and responsive interactions. Low latency LLMs can provide instant feedback and responses, creating a more seamless and engaging user experience.\\n\\n2. Better decision-making: In time-sensitive scenarios, such as financial trading or autonomous vehicles, low latency LLMs can quickly process and analyze vast amounts of data, enabling faster and more informed decision-making.\\n\\n3. Enhanced accessibility: For individuals with disabilities, low latency LLMs can help create more responsive and inclusive interfaces, such as voice-controlled assistants or real-time captioning systems.\\n\\n4. Competitive advantage: In industries where real-time data analysis and decision-making are crucial, low latency LLMs can provide a competitive edge by enabling businesses to react more quickly to market changes, customer needs, or emerging opportunities.\\n\\n5. Scalability: Low latency LLMs can efficiently handle a higher volume of requests and interactions, making them more suitable for large-scale applications and services.\\n\\nIn summary, low latency is an essential aspect of LLMs, as it significantly impacts user experience, decision-making, accessibility, competitiveness, and scalability. By minimizing delays and response times, low latency LLMs can unlock new possibilities and applications for artificial intelligence in various industries and scenarios.')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = \"You are a helpful assistant.\"\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke({\n",
+    "    \"text\": \"Explain the importance of low latency LLMs.\"\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `ChatGroq` also supports async and streaming functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"There's a star that shines up in the sky,\\nThe Sun, that makes the day bright and spry.\\nIt rises and sets,\\nIn a daily, predictable bet,\\nGiving life to the world, oh my!\")"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatGroq(temperature=0, model_name=\"mixtral-8x7b-32768\")\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a Limerick about {topic}\")])\n",
+    "chain = prompt | chat\n",
+    "await chain.ainvoke({\"topic\": \"The Sun\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The moon's gentle glow\n",
+      "Illuminates the night sky\n",
+      "Peaceful and serene"
+     ]
+    }
+   ],
+   "source": [
+    "chat = ChatGroq(temperature=0, model_name=\"llama2-70b-4096\")\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a haiku about {topic}\")])\n",
+    "chain = prompt | chat\n",
+    "for chunk in chain.stream({\"topic\": \"The Moon\"}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/jinachat.ipynb

@@ -24,8 +24,8 @@
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain_community.chat_models import JinaChat"
+    "from langchain_community.chat_models import JinaChat\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {

docs/docs/integrations/chat/kinetica.ipynb

@@ -0,0 +1,654 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Kinetica\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kinetica SqlAssist LLM Demo\n",
+    "\n",
+    "This notebook demonstrates how to use Kinetica to transform natural language into SQL\n",
+    "and simplify the process of data retrieval. This demo is intended to show the mechanics\n",
+    "of creating and using a chain as opposed to the capabilities of the LLM.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "With the Kinetica LLM workflow you create an LLM context in the database that provides\n",
+    "information needed for infefencing that includes tables, annotations, rules, and\n",
+    "samples. Invoking ``ChatKinetica.load_messages_from_context()`` will retrieve the\n",
+    "context information from the database so that it can be used to create a chat prompt.\n",
+    "\n",
+    "The chat prompt consists of a ``SystemMessage`` and pairs of\n",
+    "``HumanMessage``/``AIMessage`` that contain the samples which are question/SQL\n",
+    "pairs. You can append pairs samples to this list but it is not intended to\n",
+    "facilitate a typical natural language conversation.\n",
+    "\n",
+    "When you create a chain from the chat prompt and execute it, the Kinetica LLM will\n",
+    "generate SQL from the input. Optionally you can use ``KineticaSqlOutputParser`` to\n",
+    "execute the SQL and return the result as a dataframe.\n",
+    "\n",
+    "Currently, 2 LLM's are supported for SQL generation: \n",
+    "\n",
+    "1. **Kinetica SQL-GPT**: This LLM is based on OpenAI ChatGPT API.\n",
+    "2. **Kinetica SqlAssist**: This LLM is purpose built to integrate with the Kinetica\n",
+    "   database and it can run in a secure customer premise.\n",
+    "\n",
+    "For this demo we will be using **SqlAssist**. See the [Kinetica Documentation\n",
+    "site](https://docs.kinetica.com/7.1/sql-gpt/concepts/) for more information.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "To get started you will need a Kinetica DB instance. If you don't have one you can\n",
+    "obtain a [free development instance](https://cloud.kinetica.com/trynow).\n",
+    "\n",
+    "You will need to install the following packages..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install Langchain community and core packages\n",
+    "%pip install --upgrade --quiet langchain-core langchain-community\n",
+    "\n",
+    "# Install Kineitca DB connection package\n",
+    "%pip install --upgrade --quiet gpudb typeguard\n",
+    "\n",
+    "# Install packages needed for this tutorial\n",
+    "%pip install --upgrade --quiet faker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Database Connection\n",
+    "\n",
+    "You must set the database connection in the following environment variables. If you are using a virtual environment you can set them in the `.env` file of the project:\n",
+    "* `KINETICA_URL`: Database connection URL\n",
+    "* `KINETICA_USER`: Database user\n",
+    "* `KINETICA_PASSWD`: Secure password.\n",
+    "\n",
+    "If you can create an instance of `KineticaChatLLM` then you are successfully connected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.kinetica import ChatKinetica\n",
+    "\n",
+    "kinetica_llm = ChatKinetica()\n",
+    "\n",
+    "# Test table we will create\n",
+    "table_name = \"demo.user_profiles\"\n",
+    "\n",
+    "# LLM Context we will create\n",
+    "kinetica_ctx = \"demo.test_llm_ctx\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create test data\n",
+    "\n",
+    "Before we can generate SQL we will need to create a Kinetica table and an LLM context that can inference the table.\n",
+    "\n",
+    "### Create some fake user profiles\n",
+    "\n",
+    "We will use the `faker` package to create a dataframe with 100 fake profiles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>username</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>address</th>\n",
+       "      <th>mail</th>\n",
+       "      <th>birthdate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>eduardo69</td>\n",
+       "      <td>Haley Beck</td>\n",
+       "      <td>F</td>\n",
+       "      <td>59836 Carla Causeway Suite 939\\nPort Eugene, I...</td>\n",
+       "      <td>meltondenise@yahoo.com</td>\n",
+       "      <td>1997-09-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lbarrera</td>\n",
+       "      <td>Joshua Stephens</td>\n",
+       "      <td>M</td>\n",
+       "      <td>3108 Christina Forges\\nPort Timothychester, KY...</td>\n",
+       "      <td>erica80@hotmail.com</td>\n",
+       "      <td>1924-05-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>bburton</td>\n",
+       "      <td>Paula Kaiser</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Unit 7405 Box 3052\\nDPO AE 09858</td>\n",
+       "      <td>timothypotts@gmail.com</td>\n",
+       "      <td>1933-09-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>melissa49</td>\n",
+       "      <td>Wendy Reese</td>\n",
+       "      <td>F</td>\n",
+       "      <td>6408 Christopher Hill Apt. 459\\nNew Benjamin, ...</td>\n",
+       "      <td>dadams@gmail.com</td>\n",
+       "      <td>1988-07-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>melissacarter</td>\n",
+       "      <td>Manuel Rios</td>\n",
+       "      <td>M</td>\n",
+       "      <td>2241 Bell Gardens Suite 723\\nScottside, CA 38463</td>\n",
+       "      <td>williamayala@gmail.com</td>\n",
+       "      <td>1930-12-19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         username             name sex  \\\n",
+       "id                                       \n",
+       "0       eduardo69       Haley Beck   F   \n",
+       "1        lbarrera  Joshua Stephens   M   \n",
+       "2         bburton     Paula Kaiser   F   \n",
+       "3       melissa49      Wendy Reese   F   \n",
+       "4   melissacarter      Manuel Rios   M   \n",
+       "\n",
+       "                                              address                    mail  \\\n",
+       "id                                                                              \n",
+       "0   59836 Carla Causeway Suite 939\\nPort Eugene, I...  meltondenise@yahoo.com   \n",
+       "1   3108 Christina Forges\\nPort Timothychester, KY...     erica80@hotmail.com   \n",
+       "2                    Unit 7405 Box 3052\\nDPO AE 09858  timothypotts@gmail.com   \n",
+       "3   6408 Christopher Hill Apt. 459\\nNew Benjamin, ...        dadams@gmail.com   \n",
+       "4    2241 Bell Gardens Suite 723\\nScottside, CA 38463  williamayala@gmail.com   \n",
+       "\n",
+       "    birthdate  \n",
+       "id             \n",
+       "0  1997-09-09  \n",
+       "1  1924-05-05  \n",
+       "2  1933-09-06  \n",
+       "3  1988-07-28  \n",
+       "4  1930-12-19  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Generator\n",
+    "\n",
+    "import pandas as pd\n",
+    "from faker import Faker\n",
+    "\n",
+    "Faker.seed(5467)\n",
+    "faker = Faker(locale=\"en-US\")\n",
+    "\n",
+    "\n",
+    "def profile_gen(count: int) -> Generator:\n",
+    "    for id in range(0, count):\n",
+    "        rec = dict(id=id, **faker.simple_profile())\n",
+    "        rec[\"birthdate\"] = pd.Timestamp(rec[\"birthdate\"])\n",
+    "        yield rec\n",
+    "\n",
+    "\n",
+    "load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n",
+    "load_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a Kinetica table from the Dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>properties</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>username</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>name</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sex</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>address</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char64]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mail</td>\n",
+       "      <td>string</td>\n",
+       "      <td>[char32]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>birthdate</td>\n",
+       "      <td>long</td>\n",
+       "      <td>[timestamp]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        name    type   properties\n",
+       "0   username  string     [char32]\n",
+       "1       name  string     [char32]\n",
+       "2        sex  string      [char1]\n",
+       "3    address  string     [char64]\n",
+       "4       mail  string     [char32]\n",
+       "5  birthdate    long  [timestamp]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gpudb import GPUdbTable\n",
+    "\n",
+    "gpudb_table = GPUdbTable.from_df(\n",
+    "    load_df,\n",
+    "    db=kinetica_llm.kdbc,\n",
+    "    table_name=table_name,\n",
+    "    clear_table=True,\n",
+    "    load_data=True,\n",
+    ")\n",
+    "\n",
+    "# See the Kinetica column types\n",
+    "gpudb_table.type_as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the LLM context\n",
+    "\n",
+    "You can create an LLM Context using the Kinetica Workbench UI or you can manually create it with the `CREATE OR REPLACE CONTEXT` syntax. \n",
+    "\n",
+    "Here we create a context from the SQL syntax referencing the table we created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'status': 'OK',\n",
+       " 'message': '',\n",
+       " 'data_type': 'execute_sql_response',\n",
+       " 'response_time': 0.0148}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create an LLM context for the table.\n",
+    "\n",
+    "from gpudb import GPUdbException\n",
+    "\n",
+    "sql = f\"\"\"\n",
+    "CREATE OR REPLACE CONTEXT {kinetica_ctx}\n",
+    "(\n",
+    "    TABLE = demo.test_profiles\n",
+    "    COMMENT = 'Contains user profiles.'\n",
+    "),\n",
+    "(\n",
+    "    SAMPLES = (\n",
+    "    'How many male users are there?' = \n",
+    "    'select count(1) as num_users\n",
+    "    from demo.test_profiles\n",
+    "    where sex = ''M'';')\n",
+    ")\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def _check_error(response: dict) -> None:\n",
+    "    status = response[\"status_info\"][\"status\"]\n",
+    "    if status != \"OK\":\n",
+    "        message = response[\"status_info\"][\"message\"]\n",
+    "        raise GPUdbException(\"[%s]: %s\" % (status, message))\n",
+    "\n",
+    "\n",
+    "response = kinetica_llm.kdbc.execute_sql(sql)\n",
+    "_check_error(response)\n",
+    "response[\"status_info\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Langchain for inferencing\n",
+    "\n",
+    "In the example below we will create a chain from the previously created table and LLM context. This chain will generate SQL and return the resulting data as a dataframe.\n",
+    "\n",
+    "### Load the chat prompt from the Kinetica DB\n",
+    "\n",
+    "The `load_messages_from_context()` function will retrieve a context from the DB and convert it into a list of chat messages that we use to create a ``ChatPromptTemplate``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m System Message \u001b[0m================================\n",
+      "\n",
+      "CREATE TABLE demo.test_profiles AS\n",
+      "(\n",
+      "   username VARCHAR (32) NOT NULL,\n",
+      "   name VARCHAR (32) NOT NULL,\n",
+      "   sex VARCHAR (1) NOT NULL,\n",
+      "   address VARCHAR (64) NOT NULL,\n",
+      "   mail VARCHAR (32) NOT NULL,\n",
+      "   birthdate TIMESTAMP NOT NULL\n",
+      ");\n",
+      "COMMENT ON TABLE demo.test_profiles IS 'Contains user profiles.';\n",
+      "\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "How many male users are there?\n",
+      "\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "select count(1) as num_users\n",
+      "    from demo.test_profiles\n",
+      "    where sex = 'M';\n",
+      "\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "# load the context from the database\n",
+    "ctx_messages = kinetica_llm.load_messages_from_context(kinetica_ctx)\n",
+    "\n",
+    "# Add the input prompt. This is where input question will be substituted.\n",
+    "ctx_messages.append((\"human\", \"{input}\"))\n",
+    "\n",
+    "# Create the prompt template.\n",
+    "prompt_template = ChatPromptTemplate.from_messages(ctx_messages)\n",
+    "prompt_template.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the chain\n",
+    "\n",
+    "The last element of this chain is `KineticaSqlOutputParser` that will execute the SQL and return a dataframe. This is optional and if we left it out then only SQL would be returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.kinetica import (\n",
+    "    KineticaSqlOutputParser,\n",
+    "    KineticaSqlResponse,\n",
+    ")\n",
+    "\n",
+    "chain = prompt_template | kinetica_llm | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the SQL\n",
+    "\n",
+    "The chain we created will take a question as input and return a ``KineticaSqlResponse`` containing the generated SQL and data. The question must be relevant to the to LLM context we used to create the prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SQL: SELECT username, name\n",
+      "    FROM demo.test_profiles\n",
+      "    WHERE sex = 'F'\n",
+      "    ORDER BY username;\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>username</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>alexander40</td>\n",
+       "      <td>Tina Ramirez</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bburton</td>\n",
+       "      <td>Paula Kaiser</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>brian12</td>\n",
+       "      <td>Stefanie Williams</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>brownanna</td>\n",
+       "      <td>Jennifer Rowe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>carl19</td>\n",
+       "      <td>Amanda Potts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      username               name\n",
+       "0  alexander40       Tina Ramirez\n",
+       "1      bburton       Paula Kaiser\n",
+       "2      brian12  Stefanie Williams\n",
+       "3    brownanna      Jennifer Rowe\n",
+       "4       carl19       Amanda Potts"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here you must ask a question relevant to the LLM context provided in the prompt template.\n",
+    "response: KineticaSqlResponse = chain.invoke(\n",
+    "    {\"input\": \"What are the female users ordered by username?\"}\n",
+    ")\n",
+    "\n",
+    "print(f\"SQL: {response.sql}\")\n",
+    "response.dataframe.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "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": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/konko.ipynb

@@ -40,8 +40,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain_community.chat_models import ChatKonko"
+    "from langchain_community.chat_models import ChatKonko\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {

docs/docs/integrations/chat/litellm.ipynb

@@ -32,8 +32,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ChatLiteLLM"
+    "from langchain_community.chat_models import ChatLiteLLM\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/litellm_router.ipynb

@@ -38,8 +38,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models import ChatLiteLLMRouter\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from litellm import Router"
    ]
   },

docs/docs/integrations/chat/llama2_chat.ipynb

@@ -54,7 +54,7 @@
     "    HumanMessagePromptTemplate,\n",
     "    MessagesPlaceholder,\n",
     ")\n",
-    "from langchain.schema import SystemMessage\n",
+    "from langchain_core.messages import SystemMessage\n",
     "\n",
     "template_messages = [\n",
     "    SystemMessage(content=\"You are a helpful assistant.\"),\n",

docs/docs/integrations/chat/minimax.ipynb

@@ -39,8 +39,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import MiniMaxChat"
+    "from langchain_community.chat_models import MiniMaxChat\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/ollama.ipynb

@@ -278,7 +278,7 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "messages = [\n",
     "    HumanMessage(\n",
@@ -313,8 +313,8 @@
    "source": [
     "import json\n",
     "\n",
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -463,8 +463,8 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "llm = ChatOllama(model=\"bakllava\", temperature=0)\n",
     "\n",

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -102,7 +102,7 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "model.invoke(\"what is the weather in Boston?\")"
    ]

docs/docs/integrations/chat/openai.ipynb

@@ -34,7 +34,7 @@
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },

docs/docs/integrations/chat/promptlayer_chatopenai.ipynb

@@ -62,8 +62,8 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import PromptLayerChatOpenAI"
+    "from langchain_community.chat_models import PromptLayerChatOpenAI\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/sparkllm.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "\"\"\"For basic init and call\"\"\"\n",
-    "from langchain.chat_models import ChatSparkLLM\n",
-    "from langchain.schema import HumanMessage\n",
+    "from langchain_community.chat_models import ChatSparkLLM\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "chat = ChatSparkLLM(\n",
     "    spark_app_id=\"<app_id>\", spark_api_key=\"<api_key>\", spark_api_secret=\"<api_secret>\"\n",

docs/docs/integrations/chat/tencent_hunyuan.ipynb

@@ -36,8 +36,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ChatHunyuan"
+    "from langchain_community.chat_models import ChatHunyuan\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/tongyi.ipynb

@@ -100,8 +100,8 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.chat_models.tongyi import ChatTongyi\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "chatLLM = ChatTongyi(\n",
     "    streaming=True,\n",
@@ -128,7 +128,7 @@
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(\n",

docs/docs/integrations/chat/vllm.ipynb

@@ -36,7 +36,7 @@
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },

docs/docs/integrations/chat/volcengine_maas.ipynb

@@ -48,8 +48,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import VolcEngineMaasChat"
+    "from langchain_community.chat_models import VolcEngineMaasChat\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {

docs/docs/integrations/chat/yandex.ipynb

@@ -58,8 +58,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain_community.chat_models import ChatYandexGPT"
+    "from langchain_community.chat_models import ChatYandexGPT\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {

docs/docs/integrations/chat_loaders/discord.ipynb

@@ -79,8 +79,8 @@
     "import re\n",
     "from typing import Iterator, List\n",
     "\n",
-    "from langchain.schema import BaseMessage, HumanMessage\n",
     "from langchain_community.chat_loaders import base as chat_loaders\n",
+    "from langchain_core.messages import BaseMessage, HumanMessage\n",
     "\n",
     "logger = logging.getLogger()\n",
     "\n",

docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb

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

docs/docs/integrations/chat_loaders/twitter.ipynb

@@ -22,7 +22,7 @@
     "import json\n",
     "\n",
     "from langchain.adapters.openai import convert_message_to_dict\n",
-    "from langchain.schema import AIMessage"
+    "from langchain_core.messages import AIMessage"
    ]
   },
   {

docs/docs/integrations/chat_loaders/wechat.ipynb

@@ -78,8 +78,8 @@
     "import re\n",
     "from typing import Iterator, List\n",
     "\n",
-    "from langchain.schema import BaseMessage, HumanMessage\n",
     "from langchain_community.chat_loaders import base as chat_loaders\n",
+    "from langchain_core.messages import BaseMessage, HumanMessage\n",
     "\n",
     "logger = logging.getLogger()\n",
     "\n",

docs/docs/integrations/document_loaders/cassandra.ipynb

@@ -72,57 +72,72 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Init from a cassandra driver Session\n",
     "\n",
     "You need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "from cassandra.cluster import Cluster\n",
     "\n",
     "cluster = Cluster()\n",
     "session = cluster.connect()"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "You need to provide the name of an existing keyspace of the Cassandra instance:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "Creating the document loader:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -144,18 +159,21 @@
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "docs = loader.load()"
-   ],
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-01-19T15:47:26.399472Z",
      "start_time": "2024-01-19T15:47:26.389145Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "execution_count": 17
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
   },
   {
    "cell_type": "code",
@@ -169,7 +187,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\"  the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})"
+      "text/plain": [
+       "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\"  the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})"
+      ]
      },
      "execution_count": 19,
      "metadata": {},
@@ -182,17 +202,27 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Init from cassio\n",
     "\n",
     "It's also possible to use cassio to configure the session and keyspace."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "import cassio\n",
@@ -204,11 +234,16 @@
     ")\n",
     "\n",
     "docs = loader.load()"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Attribution statement\n",
+    "\n",
+    "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
+   ]
   }
  ],
  "metadata": {
@@ -233,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/tensorflow_datasets.ipynb

@@ -198,8 +198,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
     "from langchain_community.document_loaders import TensorflowDatasetLoader\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "loader = TensorflowDatasetLoader(\n",
     "    dataset_name=\"mlqa/en\",\n",

docs/docs/integrations/document_loaders/tidb.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TiDB\n",
+    "\n",
+    "> [TiDB](https://github.com/pingcap/tidb) is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics.\n",
+    "\n",
+    "This notebook introduces how to use `TiDBLoader` to load data from TiDB in langchain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "Before using the `TiDBLoader`, we will install the following dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we will configure the connection to a TiDB. In this notebook, we will follow the standard connection method provided by TiDB Cloud to establish a secure and efficient database connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "\n",
+    "# copy from tidb cloud console，replace it with your own\n",
+    "tidb_connection_string_template = \"mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true\"\n",
+    "tidb_password = getpass.getpass(\"Input your TiDB password:\")\n",
+    "tidb_connection_string = tidb_connection_string_template.replace(\n",
+    "    \"<PASSWORD>\", tidb_password\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data from TiDB\n",
+    "\n",
+    "Here's a breakdown of some key arguments you can use to customize the behavior of the `TiDBLoader`:\n",
+    "\n",
+    "- `query` (str): This is the SQL query to be executed against the TiDB database. The query should select the data you want to load into your `Document` objects. \n",
+    "    For instance, you might use a query like `\"SELECT * FROM my_table\"` to fetch all data from `my_table`.\n",
+    "\n",
+    "- `page_content_columns` (Optional[List[str]]): Specifies the list of column names whose values should be included in the `page_content` of each `Document` object. \n",
+    "    If set to `None` (the default), all columns returned by the query are included in `page_content`. This allows you to tailor the content of each document based on specific columns of your data.\n",
+    "\n",
+    "- `metadata_columns` (Optional[List[str]]): Specifies the list of column names whose values should be included in the `metadata` of each `Document` object. \n",
+    "    By default, this list is empty, meaning no metadata will be included unless explicitly specified. This is useful for including additional information about each document that doesn't form part of the main content but is still valuable for processing or analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine\n",
+    "\n",
+    "# Connect to the database\n",
+    "engine = create_engine(tidb_connection_string)\n",
+    "metadata = MetaData()\n",
+    "table_name = \"test_tidb_loader\"\n",
+    "\n",
+    "# Create a table\n",
+    "test_table = Table(\n",
+    "    table_name,\n",
+    "    metadata,\n",
+    "    Column(\"id\", Integer, primary_key=True),\n",
+    "    Column(\"name\", String(255)),\n",
+    "    Column(\"description\", String(255)),\n",
+    ")\n",
+    "metadata.create_all(engine)\n",
+    "\n",
+    "\n",
+    "with engine.connect() as connection:\n",
+    "    transaction = connection.begin()\n",
+    "    try:\n",
+    "        connection.execute(\n",
+    "            test_table.insert(),\n",
+    "            [\n",
+    "                {\"name\": \"Item 1\", \"description\": \"Description of Item 1\"},\n",
+    "                {\"name\": \"Item 2\", \"description\": \"Description of Item 2\"},\n",
+    "                {\"name\": \"Item 3\", \"description\": \"Description of Item 3\"},\n",
+    "            ],\n",
+    "        )\n",
+    "        transaction.commit()\n",
+    "    except:\n",
+    "        transaction.rollback()\n",
+    "        raise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------\n",
+      "content: name: Item 1\n",
+      "description: Description of Item 1\n",
+      "metada: {'id': 1}\n",
+      "------------------------------\n",
+      "content: name: Item 2\n",
+      "description: Description of Item 2\n",
+      "metada: {'id': 2}\n",
+      "------------------------------\n",
+      "content: name: Item 3\n",
+      "description: Description of Item 3\n",
+      "metada: {'id': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_community.document_loaders import TiDBLoader\n",
+    "\n",
+    "# Setup TiDBLoader to retrieve data\n",
+    "loader = TiDBLoader(\n",
+    "    connection_string=tidb_connection_string,\n",
+    "    query=f\"SELECT * FROM {table_name};\",\n",
+    "    page_content_columns=[\"name\", \"description\"],\n",
+    "    metadata_columns=[\"id\"],\n",
+    ")\n",
+    "\n",
+    "# Load data\n",
+    "documents = loader.load()\n",
+    "\n",
+    "# Display the loaded documents\n",
+    "for doc in documents:\n",
+    "    print(\"-\" * 30)\n",
+    "    print(f\"content: {doc.page_content}\\nmetada: {doc.metadata}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_table.drop(bind=engine)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_transformers/doctran_extract_properties.ipynb

@@ -32,8 +32,8 @@
    "source": [
     "import json\n",
     "\n",
-    "from langchain.schema import Document\n",
-    "from langchain_community.document_transformers import DoctranPropertyExtractor"
+    "from langchain_community.document_transformers import DoctranPropertyExtractor\n",
+    "from langchain_core.documents import Document"
    ]
   },
   {

docs/docs/integrations/document_transformers/doctran_interrogate_document.ipynb

@@ -30,8 +30,8 @@
    "source": [
     "import json\n",
     "\n",
-    "from langchain.schema import Document\n",
-    "from langchain_community.document_transformers import DoctranQATransformer"
+    "from langchain_community.document_transformers import DoctranQATransformer\n",
+    "from langchain_core.documents import Document"
    ]
   },
   {

docs/docs/integrations/document_transformers/doctran_translate_document.ipynb

@@ -28,8 +28,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
-    "from langchain_community.document_transformers import DoctranTextTranslator"
+    "from langchain_community.document_transformers import DoctranTextTranslator\n",
+    "from langchain_core.documents import Document"
    ]
   },
   {

docs/docs/integrations/document_transformers/google_translate.ipynb

@@ -31,8 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
-    "from langchain_community.document_transformers import GoogleTranslateTransformer"
+    "from langchain_community.document_transformers import GoogleTranslateTransformer\n",
+    "from langchain_core.documents import Document"
    ]
   },
   {

docs/docs/integrations/document_transformers/openai_metadata_tagger.ipynb

@@ -21,10 +21,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import Document\n",
     "from langchain_community.document_transformers.openai_functions import (\n",
     "    create_metadata_tagger,\n",
     ")\n",
+    "from langchain_core.documents import Document\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },

docs/docs/integrations/llms/ai21.ipynb

@@ -1,137 +1,114 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "602a52a4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AI21 Labs\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9597802c",
    "metadata": {},
    "source": [
-    "# AI21\n",
+    "# AI21LLM\n",
     "\n",
-    "[AI21 Studio](https://docs.ai21.com/) provides API access to `Jurassic-2` large language models.\n",
+    "This example goes over how to use LangChain to interact with `AI21` models.\n",
     "\n",
-    "This example goes over how to use LangChain to interact with [AI21 models](https://docs.ai21.com/docs/jurassic-2-models)."
+    "## Installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "02be122d-04e8-4ec6-84d1-f1d8961d6828",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# install the package:\n",
-    "%pip install --upgrade --quiet  ai21"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4229227e-6ca2-41ad-a3c3-5f29e3559091",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "id": "59c710c4",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# get AI21_API_KEY. Use https://studio.ai21.com/account/account\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "AI21_API_KEY = getpass()"
+    "!pip install -qU langchain-ai21"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6fb585dd",
+   "cell_type": "markdown",
+   "id": "560a2f9254963fd7",
    "metadata": {
-    "tags": []
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
-    "from langchain_community.llms import AI21\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "## Environment Setup\n",
+    "\n",
+    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "035dea0f",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "template = \"\"\"Question: {question}\n",
+    "import os\n",
+    "from getpass import getpass\n",
     "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(template)"
+    "os.environ[\"AI21_API_KEY\"] = getpass()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3f3458d9",
+   "cell_type": "markdown",
+   "id": "1891df96eb076e1a",
    "metadata": {
-    "tags": []
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
-    "llm = AI21(ai21_api_key=AI21_API_KEY)"
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "a641dbd9",
+   "execution_count": 6,
+   "id": "98f70927a87e4745",
    "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm_chain = prompt | llm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f0b1960",
-   "metadata": {
-    "tags": []
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\nThe Super Bowl in the year Justin Beiber was born was in the year 1991.\\nThe Super Bowl in 1991 was won by the Washington Redskins.\\nFinal answer: Washington Redskins'"
+       "'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "from langchain_ai21 import AI21LLM\n",
+    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
-    "llm_chain.invoke({\"question\": question})"
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "\n",
+    "model = AI21LLM(model=\"j2-ultra\")\n",
+    "\n",
+    "chain = prompt | model\n",
+    "\n",
+    "chain.invoke({\"question\": \"What is LangChain?\"})"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22bce013",
+   "id": "a52f765c",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -139,7 +116,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.11.1 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -153,7 +130,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/azure_ml.ipynb

@@ -70,11 +70,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.llms.azureml_endpoint import (\n",
     "    AzureMLEndpointApiType,\n",
     "    LlamaContentFormatter,\n",
     ")\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "llm = AzureMLOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/score\",\n",
@@ -117,11 +117,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
     "from langchain_community.llms.azureml_endpoint import (\n",
     "    AzureMLEndpointApiType,\n",
     "    LlamaContentFormatter,\n",
     ")\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "llm = AzureMLOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions\",\n",

docs/docs/integrations/llms/huggingface_endpoint.ipynb

@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Huggingface Endpoints\n",
+    "\n",
+    ">The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
+    "\n",
+    "The `Hugging Face Hub` also offers various endpoints to build ML applications.\n",
+    "This example showcases how to connect to the different Endpoints types.\n",
+    "\n",
+    "In particular, text generation inference is powered by [Text Generation Inference](https://github.com/huggingface/text-generation-inference): a custom-built Rust, Python and gRPC server for blazing-faset text generation inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import HuggingFaceEndpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "HUGGINGFACEHUB_API_TOKEN = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import HuggingFaceEndpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples\n",
+    "\n",
+    "Here is an example of how you can access `HuggingFaceEndpoint` integration of the free [Serverless Endpoints](https://huggingface.co/inference-endpoints/serverless) API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "repo_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
+    "\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN\n",
+    ")\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dedicated Endpoint\n",
+    "\n",
+    "\n",
+    "The free serverless API lets you implement solutions and iterate in no time, but it may be rate limited for heavy use cases, since the loads are shared with other requests.\n",
+    "\n",
+    "For enterprise workloads, the best is to use [Inference Endpoints - Dedicated](https://huggingface.co/inference-endpoints/dedicated).\n",
+    "This gives access to a fully managed infrastructure that offer more flexibility and speed. These resoucres come with continuous support and uptime guarantees, as well as options like AutoScaling\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the url to your Inference Endpoint below\n",
+    "your_endpoint_url = \"https://fayjubiy2xqn36z0.us-east-1.aws.endpoints.huggingface.cloud\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = HuggingFaceEndpoint(\n",
+    "    endpoint_url=f\"{your_endpoint_url}\",\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=10,\n",
+    "    top_p=0.95,\n",
+    "    typical_p=0.95,\n",
+    "    temperature=0.01,\n",
+    "    repetition_penalty=1.03,\n",
+    ")\n",
+    "llm(\"What did foo say about bar?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain_community.llms import HuggingFaceEndpoint\n",
+    "\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    endpoint_url=f\"{your_endpoint_url}\",\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=10,\n",
+    "    top_p=0.95,\n",
+    "    typical_p=0.95,\n",
+    "    temperature=0.01,\n",
+    "    repetition_penalty=1.03,\n",
+    "    streaming=True,\n",
+    ")\n",
+    "llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "agents",
+   "language": "python",
+   "name": "agents"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/llms/huggingface_hub.ipynb

@@ -1,466 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "959300d4",
-   "metadata": {},
-   "source": [
-    "# Hugging Face Hub\n",
-    "\n",
-    ">The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
-    "\n",
-    "This example showcases how to connect to the `Hugging Face Hub` and use different models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ddafc6d-7d7c-48fa-838f-0e7f50895ce3",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d772b637-de00-4663-bd77-9bc96d798db2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  huggingface_hub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d597a792-354c-4ca5-b483-5965eec5d63d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "HUGGINGFACEHUB_API_TOKEN = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84dd44c1-c428-41f3-a911-520281386c94",
-   "metadata": {},
-   "source": [
-    "## Prepare Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fe7d1d1-241d-426a-acff-e208f1088871",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import HuggingFaceHub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6620f39b-3d32-4840-8931-ff7d2c3e47e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain.prompts import PromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "44adc1a0-9c0a-4f1e-af5a-fe04222e78d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(template)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
-   "metadata": {},
-   "source": [
-    "## Examples\n",
-    "\n",
-    "Below are some examples of models you can access through the `Hugging Face Hub` integration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c16fded-70d1-42af-8bfa-6ddda9f0bc63",
-   "metadata": {},
-   "source": [
-    "### `Flan`, by `Google`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "repo_id = \"google/flan-t5-xxl\"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3acf0069",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The FIFA World Cup was held in the year 1994. West Germany won the FIFA World Cup in 1994\n"
-     ]
-    }
-   ],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
-   "metadata": {},
-   "source": [
-    "### `Dolly`, by `Databricks`\n",
-    "\n",
-    "See [Databricks](https://huggingface.co/databricks) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "521fcd2b-8e38-4920-b407-5c7d330411c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"databricks/dolly-v2-3b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " First of all, the world cup was won by the Germany. Then the Argentina won the world cup in 2022. So, the Argentina won the world cup in 1994.\n",
-      "\n",
-      "\n",
-      "Question: Who\n"
-     ]
-    }
-   ],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
-   "metadata": {},
-   "source": [
-    "### `Camel`, by `Writer`\n",
-    "\n",
-    "See [Writer's](https://huggingface.co/Writer) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "repo_id = \"Writer/camel-5b-hf\"  # See https://huggingface.co/Writer for other options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b06f6838-a11a-4d6a-88e3-91fa1747a2b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bf838eb-1083-402f-b099-b07c452418c8",
-   "metadata": {},
-   "source": [
-    "### `XGen`, by `Salesforce`\n",
-    "\n",
-    "See [more information](https://github.com/salesforce/xgen)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "18c78880-65d7-41d0-9722-18090efb60e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"Salesforce/xgen-7b-8k-base\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b1150b4-ec30-4674-849e-6a41b085aa2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0aca9f9e-f333-449c-97b2-10d1dbf17e75",
-   "metadata": {},
-   "source": [
-    "### `Falcon`, by `Technology Innovation Institute (TII)`\n",
-    "\n",
-    "See [more information](https://huggingface.co/tiiuae/falcon-40b)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "496b35ac-5ee2-4b68-a6ce-232608f56c03",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"tiiuae/falcon-40b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff2541ad-e394-4179-93c2-7ae9c4ca2a25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e15849b-5561-4bb9-86ec-6412ca10196a",
-   "metadata": {},
-   "source": [
-    "### `InternLM-Chat`, by `Shanghai AI Laboratory`\n",
-    "\n",
-    "See [more information](https://huggingface.co/internlm/internlm-7b)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "3b533461-59f8-406e-907b-000841fa60a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"internlm/internlm-chat-7b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c71210b9-5895-41a2-889a-f430d22fa1aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.8}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f2e5132-1713-42d7-919a-8c313744ce95",
-   "metadata": {},
-   "source": [
-    "### `Qwen`, by `Alibaba Cloud`\n",
-    "\n",
-    ">`Tongyi Qianwen-7B` (`Qwen-7B`) is a model with a scale of 7 billion parameters in the `Tongyi Qianwen` large model series developed by `Alibaba Cloud`. `Qwen-7B` is a large language model based on Transformer, which is trained on ultra-large-scale pre-training data.\n",
-    "\n",
-    "See [more information on HuggingFace](https://huggingface.co/Qwen/Qwen-7B) of on [GitHub](https://github.com/QwenLM/Qwen-7B).\n",
-    "\n",
-    "See here a [big example for LangChain integration and Qwen](https://github.com/QwenLM/Qwen-7B/blob/main/examples/langchain_tooluse.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f598b1ca-77c7-40f1-a83f-c21ea9910c88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"Qwen/Qwen-7B\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c97f4e2-d401-44fb-9da7-b60b2e2cc663",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3871376-ed0e-49a8-8d9b-7e60dbbd2b35",
-   "metadata": {},
-   "source": [
-    "### `Yi` series models, by `01.ai`\n",
-    "\n",
-    ">The `Yi` series models are large language models trained from scratch by developers at [01.ai](https://01.ai/). The first public release contains two bilingual(English/Chinese) base models with the parameter sizes of 6B(`Yi-6B`) and 34B(`Yi-34B`). Both of them are trained with 4K sequence length and can be extended to 32K during inference time. The `Yi-6B-200K` and `Yi-34B-200K` are base model with 200K context length.\n",
-    "\n",
-    "Here we test the [Yi-34B](https://huggingface.co/01-ai/Yi-34B) model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1c9d3125-3f50-48b8-93b6-b50847207afa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"01-ai/Yi-34B\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b661069-8229-4850-9f13-c4ca28c0c96b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd6f3edc-9f97-47a6-ab2c-116756babbe6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/llms/huggingface_textgen_inference.ipynb

@@ -1,108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huggingface TextGen Inference\n",
-    "\n",
-    "[Text Generation Inference](https://github.com/huggingface/text-generation-inference) is a Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co/) to power LLMs api-inference widgets.\n",
-    "\n",
-    "This notebooks goes over how to use a self hosted LLM using `Text Generation Inference`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To use, you should have the `text_generation` python package installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# !pip3 install text_generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import HuggingFaceTextGenInference\n",
-    "\n",
-    "llm = HuggingFaceTextGenInference(\n",
-    "    inference_server_url=\"http://localhost:8010/\",\n",
-    "    max_new_tokens=512,\n",
-    "    top_k=10,\n",
-    "    top_p=0.95,\n",
-    "    typical_p=0.95,\n",
-    "    temperature=0.01,\n",
-    "    repetition_penalty=1.03,\n",
-    ")\n",
-    "llm(\"What did foo say about bar?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain_community.llms import HuggingFaceTextGenInference\n",
-    "\n",
-    "llm = HuggingFaceTextGenInference(\n",
-    "    inference_server_url=\"http://localhost:8010/\",\n",
-    "    max_new_tokens=512,\n",
-    "    top_k=10,\n",
-    "    top_p=0.95,\n",
-    "    typical_p=0.95,\n",
-    "    temperature=0.01,\n",
-    "    repetition_penalty=1.03,\n",
-    "    streaming=True,\n",
-    ")\n",
-    "llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}