fmt

.github/CONTRIBUTING.md

@@ -3,4 +3,43 @@
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
-To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/).
+To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
+
+## 🗺️ Guidelines
+
+### 👩‍💻 Ways to contribute
+
+There are many ways to contribute to LangChain. Here are some common ways people contribute:
+
+- [**Documentation**](https://python.langchain.com/docs/contributing/documentation): Help improve our docs, including this one!
+- [**Code**](https://python.langchain.com/docs/contributing/code): Help us write code, fix bugs, or improve our infrastructure.
+- [**Integrations**](https://python.langchain.com/docs/contributing/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+with the rapid rate of development in this field some may get out of date.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/)

.github/DISCUSSION_TEMPLATE/q-a.yml

@@ -3,18 +3,18 @@ body:
   - type: markdown
     attributes:
       value: |
-        Thanks for your interest in LangChain 🦜️🔗!
+        Thanks for your interest in 🦜️🔗 LangChain!
 
         Please follow these instructions, fill every question, and do every step. 🙏
         
         We're asking for this because answering questions and solving problems in GitHub takes a lot of time --
-        this is time that we cannot spend on adding new features, fixing bugs, writing documentation or reviewing pull requests.
+        this is time that we cannot spend on adding new features, fixing bugs, write documentation or reviewing pull requests.
 
-        By asking questions in a structured way (following this) it will be much easier for us to help you.
+        By asking questions in a structured way (following this) it will be much easier to help you.
 
-        There's a high chance that by following this process, you'll find the solution on your own, eliminating the need to submit a question and wait for an answer. 😎
+        And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎
 
-        As there are many questions submitted every day, we will **DISCARD** and close the incomplete ones. 
+        As there are too many questions, we will **DISCARD** and close the incomplete ones. 
         
         That will allow us (and others) to focus on helping people like you that follow the whole process. 🤓
         

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -35,8 +35,6 @@ body:
           required: true
         - label: I am sure that this is a bug in LangChain rather than my code.
           required: true
-        - label: The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).
-          required: true
   - type: textarea
     id: reproduction
     validations:

.github/ISSUE_TEMPLATE/documentation.yml

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

.github/ISSUE_TEMPLATE/privileged.yml

@@ -9,7 +9,7 @@ body:
         If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
         
         You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
-        or are a regular contributor to LangChain with previous merged pull requests.
+        or are a regular contributor to LangChain with previous merged merged pull requests.
   - type: checkboxes
     id: privileged
     attributes:

.github/PULL_REQUEST_TEMPLATE.md

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

.github/actions/people/Dockerfile

@@ -1,7 +0,0 @@
-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

@@ -1,11 +0,0 @@
-# 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

@@ -1,641 +0,0 @@
-# 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

@@ -1,23 +1,17 @@
 import json
 import sys
 import os
-from typing import Dict
 
-LANGCHAIN_DIRS = [
+LANGCHAIN_DIRS = {
     "libs/core",
     "libs/langchain",
     "libs/experimental",
     "libs/community",
-]
+}
 
 if __name__ == "__main__":
     files = sys.argv[1:]
-
-    dirs_to_run: Dict[str, set] = {
-        "lint": set(),
-        "test": set(),
-        "extended-test": set(),
-    }
+    dirs_to_run = set()
 
     if len(files) == 300:
         # max diff length is 300 files - there are likely files missing
@@ -30,42 +24,27 @@ if __name__ == "__main__":
                 ".github/workflows",
                 ".github/tools",
                 ".github/actions",
+                "libs/core",
                 ".github/scripts/check_diff.py",
             )
         ):
-            # add all LANGCHAIN_DIRS for infra changes
-            dirs_to_run["extended-test"].update(LANGCHAIN_DIRS)
-            dirs_to_run["lint"].add(".")
-
-        if any(file.startswith(dir_) for dir_ in LANGCHAIN_DIRS):
-            # add that dir and all dirs after in LANGCHAIN_DIRS
-            # for extended testing
-            found = False
-            for dir_ in LANGCHAIN_DIRS:
-                if file.startswith(dir_):
-                    found = True
-                if found:
-                    dirs_to_run["extended-test"].add(dir_)
-        elif file.startswith("libs/partners"):
+            dirs_to_run.update(LANGCHAIN_DIRS)
+        elif "libs/community" in file:
+            dirs_to_run.update(
+                ("libs/community", "libs/langchain", "libs/experimental")
+            )
+        elif "libs/partners" in file:
             partner_dir = file.split("/")[2]
             if os.path.isdir(f"libs/partners/{partner_dir}"):
-                dirs_to_run["test"].add(f"libs/partners/{partner_dir}")
+                dirs_to_run.add(f"libs/partners/{partner_dir}")
             # Skip if the directory was deleted
+        elif "libs/langchain" in file:
+            dirs_to_run.update(("libs/langchain", "libs/experimental"))
+        elif "libs/experimental" in file:
+            dirs_to_run.add("libs/experimental")
         elif file.startswith("libs/"):
-            raise ValueError(
-                f"Unknown lib: {file}. check_diff.py likely needs "
-                "an update for this new library!"
-            )
-        elif any(file.startswith(p) for p in ["docs/", "templates/", "cookbook/"]):
-            dirs_to_run["lint"].add(".")
-
-    outputs = {
-        "dirs-to-lint": list(
-            dirs_to_run["lint"] | dirs_to_run["test"] | dirs_to_run["extended-test"]
-        ),
-        "dirs-to-test": list(dirs_to_run["test"] | dirs_to_run["extended-test"]),
-        "dirs-to-extended-test": list(dirs_to_run["extended-test"]),
-    }
-    for key, value in outputs.items():
-        json_output = json.dumps(value)
-        print(f"{key}={json_output}")  # noqa: T201
+            dirs_to_run.update(LANGCHAIN_DIRS)
+        else:
+            pass
+    json_output = json.dumps(list(dirs_to_run))
+    print(f"dirs-to-run={json_output}")

.github/scripts/get_min_versions.py

@@ -62,6 +62,4 @@ toml_file = sys.argv[1]
 # Call the function to get the minimum versions
 min_versions = get_min_version_from_toml(toml_file)
 
-print(
-    " ".join([f"{lib}=={version}" for lib, version in min_versions.items()])
-)  # noqa: T201
+print(" ".join([f"{lib}=={version}" for lib, version in min_versions.items()]))

.github/workflows/_all_ci.yml

@@ -0,0 +1,110 @@
+---
+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/_dependencies.yml

@@ -63,8 +63,6 @@ jobs:
       - name: Install the opposite major version of pydantic
         # If normal tests use pydantic v1, here we'll use v2, and vice versa.
         shell: bash
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
         run: |
           # Determine the major part of pydantic version
           REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
@@ -99,8 +97,6 @@ jobs:
           fi
           echo "Found pydantic version ${CURRENT_VERSION}, as expected"
       - name: Run pydantic compatibility tests
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
         shell: bash
         run: make test
 

.github/workflows/_integration_test.yml

@@ -52,7 +52,6 @@ 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 }}
@@ -63,17 +62,8 @@ jobs:
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
-          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
-          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
           PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
           PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
-          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
-          ES_URL: ${{ secrets.ES_URL }}
-          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
-          ES_API_KEY: ${{ secrets.ES_API_KEY }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # for airbyte
         run: |
           make integration_tests
 

.github/workflows/_release.yml

@@ -166,35 +166,18 @@ jobs:
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
-          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
-          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 }}
-          ES_URL: ${{ secrets.ES_URL }}
-          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
-          ES_API_KEY: ${{ secrets.ES_API_KEY }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # for airbyte
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/api_doc_build.yml

@@ -1,69 +0,0 @@
-name: API docs build
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron:  '0 13 * * *'
-env:
-  POETRY_VERSION: "1.7.1"
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: bagatur/api_docs_build
-          path: langchain
-      - uses: actions/checkout@v4
-        with:
-          repository: langchain-ai/langchain-google
-          path: langchain-google
-      - name: Move google libs
-        run: |
-          rm -rf langchain/libs/partners/google-genai langchain/libs/partners/google-vertexai
-          mv langchain-google/libs/genai langchain/libs/partners/google-genai
-          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
-
-      - name: Set Git config
-        working-directory: langchain
-        run: |
-          git config --local user.email "actions@github.com"
-          git config --local user.name "Github Actions"
-
-      - name: Merge master
-        working-directory: langchain
-        run: | 
-          git fetch origin master
-          git merge origin/master -m "Merge master" --allow-unrelated-histories -X theirs
-
-      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./langchain/.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: api-docs
-          working-directory: langchain
-
-      - name: Install dependencies
-        working-directory: langchain
-        run: |
-          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
-          poetry run python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
-          # skip airbyte and ibm due to pandas dependency issue
-          poetry run python -m pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
-          poetry run python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
-
-      - name: Build docs
-        working-directory: langchain
-        run: |
-          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
-          poetry run python docs/api_reference/create_api_rst.py
-          poetry run python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference api_reference_build/html -j auto
-
-      # https://github.com/marketplace/actions/add-commit
-      - uses: EndBug/add-and-commit@v9
-        with:
-          cwd: langchain
-          message: 'Update API docs build'

.github/workflows/check_diffs.yml

@@ -16,9 +16,6 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-env:
-  POETRY_VERSION: "1.7.1"
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -33,125 +30,15 @@ jobs:
         run: |
           python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
-      dirs-to-lint: ${{ steps.set-matrix.outputs.dirs-to-lint }}
-      dirs-to-test: ${{ steps.set-matrix.outputs.dirs-to-test }}
-      dirs-to-extended-test: ${{ steps.set-matrix.outputs.dirs-to-extended-test }}
-  lint:
+      dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }}
+  ci:
     name: cd ${{ matrix.working-directory }}
     needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-lint != '[]' }}
     strategy:
       matrix:
-        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-lint) }}
-    uses: ./.github/workflows/_lint.yml
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
+    uses: ./.github/workflows/_all_ci.yml
     with:
       working-directory: ${{ matrix.working-directory }}
-    secrets: inherit
 
-  test:
-    name: cd ${{ matrix.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
-    strategy:
-      matrix:
-        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
-    uses: ./.github/workflows/_test.yml
-    with:
-      working-directory: ${{ matrix.working-directory }}
-    secrets: inherit
 
-  compile-integration-tests:
-    name: cd ${{ matrix.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
-    strategy:
-      matrix:
-        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
-    uses: ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: ${{ matrix.working-directory }}
-    secrets: inherit
-
-  dependencies:
-    name: cd ${{ matrix.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
-    strategy:
-      matrix:
-        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-test) }}
-    uses: ./.github/workflows/_dependencies.yml
-    with:
-      working-directory: ${{ matrix.working-directory }}
-    secrets: inherit
-
-  extended-tests:
-    name: "cd ${{ matrix.working-directory }} / make extended_tests #${{ matrix.python-version }}"
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-extended-test != '[]' }}
-    strategy:
-      matrix:
-        # note different variable for extended test dirs
-        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-extended-test) }}
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ${{ matrix.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: ${{ matrix.working-directory }}
-          cache-key: extended
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          echo "Running extended tests, installing dependencies with poetry..."
-          poetry install -E extended_testing --with test
-
-      - name: Run extended tests
-        run: |
-          echo "sleeping 150"
-          sleep 150
-          echo "sleeping 151"
-          sleep 151
-          echo "done sleeping lets test"
-          make extended_tests
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(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 +32,5 @@ jobs:
       - name: Codespell
         uses: codespell-project/actions-codespell@v2
         with:
-          skip: guide_imports.json,*.ambr,./cookbook/data/imdb_top_1000.csv
+          skip: guide_imports.json
           ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
-          exclude_file: libs/community/langchain_community/llms/yuan2.py

.github/workflows/doc_lint.yml

@@ -0,0 +1,37 @@
+---
+name: CI / cd .
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    paths:
+      - 'docs/**'
+      - 'templates/**'
+      - 'cookbook/**'
+      - '.github/workflows/_lint.yml'
+      - '.github/workflows/doc_lint.yml'
+  workflow_dispatch:
+
+jobs:
+  check:
+    name: Check for "from langchain import x" imports
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Run import check
+      run: |
+        # We should not encourage imports directly from main init file
+        # Expect for hub
+        git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
+
+  lint:
+      name: "-"
+      uses:
+        ./.github/workflows/_lint.yml
+      with:
+        working-directory: "."
+      secrets: inherit

.github/workflows/extract_ignored_words_list.py

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

.github/workflows/people.yml

@@ -1,36 +0,0 @@
-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

@@ -115,10 +115,13 @@ celerybeat.pid
 # Environments
 .env
 .envrc
-.venv*
+.venv
+.venvs
 env/
+venv/
 ENV/
 env.bak/
+venv.bak/
 
 # Spyder project settings
 .spyderproject
@@ -174,6 +177,4 @@ docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock
 _dist
-docs/docs/templates
-
-prof
+docs/docs/templates

.readthedocs.yaml

@@ -13,8 +13,15 @@ build:
   tools:
     python: "3.11"
   commands:
-    - mkdir -p $READTHEDOCS_OUTPUT
-    - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
+      - python -m virtualenv $READTHEDOCS_VIRTUALENV_PATH
+      - python -m pip install --upgrade --no-cache-dir pip setuptools
+      - python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
+      - python -m pip install ./libs/partners/*
+      - python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
+      - python docs/api_reference/create_api_rst.py
+      - cat docs/api_reference/conf.py
+      - python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference $READTHEDOCS_OUTPUT/html -j auto
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
    configuration: docs/api_reference/conf.py

Makefile

@@ -15,12 +15,7 @@ docs_build:
 	docs/.local_build.sh
 
 docs_clean:
-	@if [ -d _dist ]; then \
-			rm -r _dist; \
-			echo "Directory _dist has been cleaned."; \
-	else \
-			echo "Nothing to clean."; \
-	fi
+	rm -r _dist
 
 docs_linkcheck:
 	poetry run linkchecker _dist/docs/ --ignore-url node_modules
@@ -50,13 +45,11 @@ lint lint_package lint_tests:
 	poetry run ruff docs templates cookbook
 	poetry run ruff format docs templates cookbook --diff
 	poetry run ruff --select I docs templates cookbook
-	git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
 
 format format_diff:
 	poetry run ruff format docs templates cookbook
 	poetry run ruff --select I --fix docs templates cookbook
 
-
 ######################
 # HELP
 ######################

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://www.langchain.com/contact-sales) to speak with our sales team.
+Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
 
 ## Quick Install
 
@@ -43,7 +43,6 @@ This framework consists of several parts.
 - **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
 - **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
-- **[LangGraph](https://python.langchain.com/docs/langgraph)**: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. 
 
 The LangChain libraries themselves are made up of several different packages.
 - **[`langchain-core`](libs/core)**: Base abstractions and LangChain Expression Language.

cookbook/advanced_rag_eval.ipynb

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

cookbook/airbyte_github.ipynb

@@ -1,200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-airbyte"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "\n",
-    "GITHUB_TOKEN = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_airbyte import AirbyteLoader\n",
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "loader = AirbyteLoader(\n",
-    "    source=\"source-github\",\n",
-    "    stream=\"pull_requests\",\n",
-    "    config={\n",
-    "        \"credentials\": {\"personal_access_token\": GITHUB_TOKEN},\n",
-    "        \"repositories\": [\"langchain-ai/langchain\"],\n",
-    "    },\n",
-    "    template=PromptTemplate.from_template(\n",
-    "        \"\"\"# {title}\n",
-    "by {user[login]}\n",
-    "\n",
-    "{body}\"\"\"\n",
-    "    ),\n",
-    "    include_metadata=False,\n",
-    ")\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Updated partners/ibm README\n",
-      "by williamdevena\n",
-      "\n",
-      "## PR title\n",
-      "partners: changed the README file for the IBM Watson AI integration in the libs/partners/ibm folder.\n",
-      "\n",
-      "## PR message\n",
-      "Description: Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\n",
-      "\n",
-      "The README includes:\n",
-      "\n",
-      "- Brief description\n",
-      "- Installation\n",
-      "- Setting-up instructions (API key, project id, ...)\n",
-      "- Basic usage:\n",
-      "  - Loading the model\n",
-      "  - Direct inference\n",
-      "  - Chain invoking\n",
-      "  - Streaming the model output\n",
-      "  \n",
-      "Issue: https://github.com/langchain-ai/langchain/issues/17545\n",
-      "\n",
-      "Dependencies: None\n",
-      "\n",
-      "Twitter handle: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[-2].page_content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10283"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tiktoken\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "enc = tiktoken.get_encoding(\"cl100k_base\")\n",
-    "\n",
-    "vectorstore = Chroma.from_documents(\n",
-    "    docs,\n",
-    "    embedding=OpenAIEmbeddings(\n",
-    "        disallowed_special=(enc.special_tokens_set - {\"<|endofprompt|>\"})\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='# Updated partners/ibm README\\nby williamdevena\\n\\n## PR title\\r\\npartners: changed the README file for the IBM Watson AI integration in the libs/partners/ibm folder.\\r\\n\\r\\n## PR message\\r\\nDescription: Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\\r\\n\\r\\nThe README includes:\\r\\n\\r\\n- Brief description\\r\\n- Installation\\r\\n- Setting-up instructions (API key, project id, ...)\\r\\n- Basic usage:\\r\\n  - Loading the model\\r\\n  - Direct inference\\r\\n  - Chain invoking\\r\\n  - Streaming the model output\\r\\n  \\r\\nIssue: https://github.com/langchain-ai/langchain/issues/17545\\r\\n\\r\\nDependencies: None\\r\\n\\r\\nTwitter handle: None'),\n",
-       " Document(page_content='# Updated partners/ibm README\\nby williamdevena\\n\\n## PR title\\r\\npartners: changed the README file for the IBM Watson AI integration in the `libs/partners/ibm` folder. \\r\\n\\r\\n\\r\\n\\r\\n## PR message\\r\\n- **Description:** Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\\r\\n\\r\\n    The README includes:\\r\\n    - Brief description\\r\\n    - Installation\\r\\n    - Setting-up instructions (API key, project id, ...)\\r\\n    - Basic usage:\\r\\n        - Loading the model\\r\\n        - Direct inference\\r\\n        - Chain invoking\\r\\n        - Streaming the model output\\r\\n\\r\\n\\r\\n- **Issue:** #17545\\r\\n- **Dependencies:** None\\r\\n- **Twitter handle:** None'),\n",
-       " Document(page_content='# IBM: added partners package `langchain_ibm`, added llm\\nby MateuszOssGit\\n\\n  - **Description:** Added `langchain_ibm` as an langchain partners package of IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (`WatsonxLLM`)\\r\\n  - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),\\r\\n  - **Tag maintainer:** : \\r\\n\\r\\nPlease make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅'),\n",
-       " Document(page_content='# Add WatsonX support\\nby baptistebignaud\\n\\nIt is a connector to use a LLM from WatsonX.\\r\\nIt requires python SDK \"ibm-generative-ai\"\\r\\n\\r\\n(It might not be perfect since it is my first PR on a public repository 😄)')]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever.invoke(\"pull requests related to IBM\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

cookbook/amazon_personalize_how_to.ipynb

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

cookbook/apache_kafka_message_handling.ipynb

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

cookbook/fireworks_rag.ipynb

@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0fc0309d-4d49-4bb5-bec0-bd92c6fddb28",
-   "metadata": {},
-   "source": [
-    "## Fireworks.AI + LangChain + RAG\n",
-    " \n",
-    "[Fireworks AI](https://python.langchain.com/docs/integrations/llms/fireworks) wants to provide the best experience when working with LangChain, and here is an example of Fireworks + LangChain doing RAG\n",
-    "\n",
-    "See [our models page](https://fireworks.ai/models) for the full list of models. We use `accounts/fireworks/models/mixtral-8x7b-instruct` for RAG In this tutorial.\n",
-    "\n",
-    "For the RAG target, we will use the Gemma technical report https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "Found existing installation: langchain-fireworks 0.0.1\n",
-      "Uninstalling langchain-fireworks-0.0.1:\n",
-      "  Successfully uninstalled langchain-fireworks-0.0.1\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "Obtaining file:///mnt/disks/data/langchain/libs/partners/fireworks\n",
-      "  Installing build dependencies ... \u001b[?25ldone\n",
-      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
-      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: aiohttp<4.0.0,>=3.9.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (3.9.3)\n",
-      "Requirement already satisfied: fireworks-ai<0.13.0,>=0.12.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (0.12.0)\n",
-      "Requirement already satisfied: langchain-core<0.2,>=0.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (0.1.23)\n",
-      "Requirement already satisfied: requests<3,>=2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (2.31.0)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.3.1)\n",
-      "Requirement already satisfied: attrs>=17.3.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (23.1.0)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.4.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (6.0.4)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.9.2)\n",
-      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (4.0.3)\n",
-      "Requirement already satisfied: httpx in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.26.0)\n",
-      "Requirement already satisfied: httpx-sse in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.4.0)\n",
-      "Requirement already satisfied: pydantic in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (2.4.2)\n",
-      "Requirement already satisfied: Pillow in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (10.2.0)\n",
-      "Requirement already satisfied: PyYAML>=5.3 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (6.0.1)\n",
-      "Requirement already satisfied: anyio<5,>=3 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (3.7.1)\n",
-      "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.33)\n",
-      "Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (0.1.5)\n",
-      "Requirement already satisfied: packaging<24.0,>=23.2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (23.2)\n",
-      "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (8.2.3)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (3.3.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (3.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (2.0.6)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (2023.7.22)\n",
-      "Requirement already satisfied: sniffio>=1.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.3.0)\n",
-      "Requirement already satisfied: exceptiongroup in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.1.3)\n",
-      "Requirement already satisfied: jsonpointer>=1.9 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (2.4)\n",
-      "Requirement already satisfied: annotated-types>=0.4.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.5.0)\n",
-      "Requirement already satisfied: pydantic-core==2.10.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (2.10.1)\n",
-      "Requirement already satisfied: typing-extensions>=4.6.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (4.8.0)\n",
-      "Requirement already satisfied: httpcore==1.* in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from httpx->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (1.0.2)\n",
-      "Requirement already satisfied: h11<0.15,>=0.13 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from httpcore==1.*->httpx->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.14.0)\n",
-      "Building wheels for collected packages: langchain-fireworks\n",
-      "  Building editable for langchain-fireworks (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for langchain-fireworks: filename=langchain_fireworks-0.0.1-py3-none-any.whl size=2228 sha256=564071b120b09ec31f2dc737733448a33bbb26e40b49fcde0c129ad26045259d\n",
-      "  Stored in directory: /tmp/pip-ephem-wheel-cache-oz368vdk/wheels/e0/ad/31/d7e76dd73d61905ff7f369f5b0d21a4b5e7af4d3cb7487aece\n",
-      "Successfully built langchain-fireworks\n",
-      "Installing collected packages: langchain-fireworks\n",
-      "Successfully installed langchain-fireworks-0.0.1\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install --quiet pypdf chromadb tiktoken openai \n",
-    "%pip uninstall -y langchain-fireworks\n",
-    "%pip install --editable /mnt/disks/data/langchain/libs/partners/fireworks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cf719376",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<module 'fireworks' from '/mnt/disks/data/langchain/.venv/lib/python3.9/site-packages/fireworks/__init__.py'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "import fireworks\n",
-    "\n",
-    "print(fireworks)\n",
-    "import fireworks.client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ab49327-0532-4480-804c-d066c302a322",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load\n",
-    "import requests\n",
-    "from langchain_community.document_loaders import PyPDFLoader\n",
-    "\n",
-    "# Download the PDF from a URL and save it to a temporary location\n",
-    "url = \"https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf\"\n",
-    "response = requests.get(url, stream=True)\n",
-    "file_name = \"temp_file.pdf\"\n",
-    "with open(file_name, \"wb\") as pdf:\n",
-    "    pdf.write(response.content)\n",
-    "\n",
-    "loader = PyPDFLoader(file_name)\n",
-    "data = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)\n",
-    "\n",
-    "# Add to vectorDB\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_fireworks.embeddings import FireworksEmbeddings\n",
-    "\n",
-    "vectorstore = Chroma.from_documents(\n",
-    "    documents=all_splits,\n",
-    "    collection_name=\"rag-chroma\",\n",
-    "    embedding=FireworksEmbeddings(),\n",
-    ")\n",
-    "\n",
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4efaddd9-3dbb-455c-ba54-0ad7f2d2ce0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
-    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
-    "\n",
-    "# RAG prompt\n",
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "# LLM\n",
-    "from langchain_together import Together\n",
-    "\n",
-    "llm = Together(\n",
-    "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
-    "    temperature=0.0,\n",
-    "    max_tokens=2000,\n",
-    "    top_k=1,\n",
-    ")\n",
-    "\n",
-    "# RAG chain\n",
-    "chain = (\n",
-    "    RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "88b1ee51-1b0f-4ebf-bb32-e50e843f0eeb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nAnswer: The architectural details of Mixtral are as follows:\\n- Dimension (dim): 4096\\n- Number of layers (n\\\\_layers): 32\\n- Dimension of each head (head\\\\_dim): 128\\n- Hidden dimension (hidden\\\\_dim): 14336\\n- Number of heads (n\\\\_heads): 32\\n- Number of kv heads (n\\\\_kv\\\\_heads): 8\\n- Context length (context\\\\_len): 32768\\n- Vocabulary size (vocab\\\\_size): 32000\\n- Number of experts (num\\\\_experts): 8\\n- Number of top k experts (top\\\\_k\\\\_experts): 2\\n\\nMixtral is based on a transformer architecture and uses the same modifications as described in [18], with the notable exceptions that Mixtral supports a fully dense context length of 32k tokens, and the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token. Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke(\"What are the Architectural details of Mixtral?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "755cf871-26b7-4e30-8b91-9ffd698470f4",
-   "metadata": {},
-   "source": [
-    "Trace: \n",
-    "\n",
-    "https://smith.langchain.com/public/935fd642-06a6-4b42-98e3-6074f93115cd/r"
-   ]
-  }
- ],
- "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": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -73,9 +73,8 @@
     "    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_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "from pydantic import BaseModel, Field"
    ]
   },

cookbook/optimization.ipynb

@@ -1,648 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c7fe38bc",
-   "metadata": {},
-   "source": [
-    "# Optimization\n",
-    "\n",
-    "This notebook goes over how to optimize chains using LangChain and [LangSmith](https://smith.langchain.com)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f87ccd5",
-   "metadata": {},
-   "source": [
-    "## Set up\n",
-    "\n",
-    "We will set an environment variable for LangSmith, and load the relevant data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "236bedc5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LANGCHAIN_PROJECT\"] = \"movie-qa\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a3fed0dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "7cfff337",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(\"data/imdb_top_1000.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2d20fb9c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"Released_Year\"] = df[\"Released_Year\"].astype(int, errors=\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09fc8fe2",
-   "metadata": {},
-   "source": [
-    "## Create the initial retrieval chain\n",
-    "\n",
-    "We will use a self-query retriever"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f71e24e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema import Document\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8881ea8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "records = df.to_dict(\"records\")\n",
-    "documents = [Document(page_content=d[\"Overview\"], metadata=d) for d in records]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8f495423",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = Chroma.from_documents(documents, embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "31d33d62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
-    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "metadata_field_info = [\n",
-    "    AttributeInfo(\n",
-    "        name=\"Released_Year\",\n",
-    "        description=\"The year the movie was released\",\n",
-    "        type=\"int\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"Series_Title\",\n",
-    "        description=\"The title of the movie\",\n",
-    "        type=\"str\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"Genre\",\n",
-    "        description=\"The genre of the movie\",\n",
-    "        type=\"string\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"IMDB_Rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
-    "    ),\n",
-    "]\n",
-    "document_content_description = \"Brief summary of a movie\"\n",
-    "llm = ChatOpenAI(temperature=0)\n",
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a731533b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.runnables import RunnablePassthrough"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "05181849",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "feed4be6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_template(\n",
-    "    \"\"\"Answer the user's question based on the below information:\n",
-    "\n",
-    "Information:\n",
-    "\n",
-    "{info}\n",
-    "\n",
-    "Question: {question}\"\"\"\n",
-    ")\n",
-    "generator = (prompt | ChatOpenAI() | StrOutputParser()).with_config(\n",
-    "    run_name=\"generator\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "eb16cc9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    RunnablePassthrough.assign(info=(lambda x: x[\"question\"]) | retriever) | generator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c70911cc",
-   "metadata": {},
-   "source": [
-    "## Run examples\n",
-    "\n",
-    "Run examples through the chain. This can either be manually, or using a list of examples, or production traffic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "19a88d13",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'One of the horror movies released in the early 2000s is \"The Ring\" (2002), directed by Gore Verbinski.'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"question\": \"what is a horror movie released in early 2000s\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17f9cdae",
-   "metadata": {},
-   "source": [
-    "## Annotate\n",
-    "\n",
-    "Now, go to LangSmitha and annotate those examples as correct or incorrect"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e211da6",
-   "metadata": {},
-   "source": [
-    "## Create Dataset\n",
-    "\n",
-    "We can now create a dataset from those runs.\n",
-    "\n",
-    "What we will do is find the runs marked as correct, then grab the sub-chains from them. Specifically, the query generator sub chain and the final generation step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "e4024267",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langsmith import Client\n",
-    "\n",
-    "client = Client()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "3814efc5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "runs = list(\n",
-    "    client.list_runs(\n",
-    "        project_name=\"movie-qa\",\n",
-    "        execution_order=1,\n",
-    "        filter=\"and(eq(feedback_key, 'correctness'), eq(feedback_score, 1))\",\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "len(runs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "3eb123e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gen_runs = []\n",
-    "query_runs = []\n",
-    "for r in runs:\n",
-    "    gen_runs.extend(\n",
-    "        list(\n",
-    "            client.list_runs(\n",
-    "                project_name=\"movie-qa\",\n",
-    "                filter=\"eq(name, 'generator')\",\n",
-    "                trace_id=r.trace_id,\n",
-    "            )\n",
-    "        )\n",
-    "    )\n",
-    "    query_runs.extend(\n",
-    "        list(\n",
-    "            client.list_runs(\n",
-    "                project_name=\"movie-qa\",\n",
-    "                filter=\"eq(name, 'query_constructor')\",\n",
-    "                trace_id=r.trace_id,\n",
-    "            )\n",
-    "        )\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a4397026",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'what is a high school comedy released in early 2000s'}"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "runs[0].inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "3fa6ad2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output': 'One high school comedy released in the early 2000s is \"Mean Girls\" starring Lindsay Lohan, Rachel McAdams, and Tina Fey.'}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "runs[0].outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "1fda5b4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'what is a high school comedy released in early 2000s'}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query_runs[0].inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "1a1a51e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output': {'query': 'high school comedy',\n",
-       "  'filter': {'operator': 'and',\n",
-       "   'arguments': [{'comparator': 'eq', 'attribute': 'Genre', 'value': 'comedy'},\n",
-       "    {'operator': 'and',\n",
-       "     'arguments': [{'comparator': 'gte',\n",
-       "       'attribute': 'Released_Year',\n",
-       "       'value': 2000},\n",
-       "      {'comparator': 'lt', 'attribute': 'Released_Year', 'value': 2010}]}]}}}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query_runs[0].outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e9d9966b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'what is a high school comedy released in early 2000s',\n",
-       " 'info': []}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen_runs[0].inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "bc113f3d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output': 'One high school comedy released in the early 2000s is \"Mean Girls\" starring Lindsay Lohan, Rachel McAdams, and Tina Fey.'}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen_runs[0].outputs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cca74e5",
-   "metadata": {},
-   "source": [
-    "## Create datasets\n",
-    "\n",
-    "We can now create datasets for the query generation and final generation step.\n",
-    "We do this so that (1) we can inspect the datapoints, (2) we can edit them if needed, (3) we can add to them over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "69966f0e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.create_dataset(\"movie-query_constructor\")\n",
-    "\n",
-    "inputs = [r.inputs for r in query_runs]\n",
-    "outputs = [r.outputs for r in query_runs]\n",
-    "\n",
-    "client.create_examples(\n",
-    "    inputs=inputs, outputs=outputs, dataset_name=\"movie-query_constructor\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7e15770e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.create_dataset(\"movie-generator\")\n",
-    "\n",
-    "inputs = [r.inputs for r in gen_runs]\n",
-    "outputs = [r.outputs for r in gen_runs]\n",
-    "\n",
-    "client.create_examples(inputs=inputs, outputs=outputs, dataset_name=\"movie-generator\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61cf9bcd",
-   "metadata": {},
-   "source": [
-    "## Use as few shot examples\n",
-    "\n",
-    "We can now pull down a dataset and use them as few shot examples in a future chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d9c79173",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "examples = list(client.list_examples(dataset_name=\"movie-query_constructor\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "a1771dd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "\n",
-    "def filter_to_string(_filter):\n",
-    "    if \"operator\" in _filter:\n",
-    "        args = [filter_to_string(f) for f in _filter[\"arguments\"]]\n",
-    "        return f\"{_filter['operator']}({','.join(args)})\"\n",
-    "    else:\n",
-    "        comparator = _filter[\"comparator\"]\n",
-    "        attribute = json.dumps(_filter[\"attribute\"])\n",
-    "        value = json.dumps(_filter[\"value\"])\n",
-    "        return f\"{comparator}({attribute}, {value})\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "e67a3530",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_examples = []\n",
-    "\n",
-    "for e in examples:\n",
-    "    if \"filter\" in e.outputs[\"output\"]:\n",
-    "        string_filter = filter_to_string(e.outputs[\"output\"][\"filter\"])\n",
-    "    else:\n",
-    "        string_filter = \"NO_FILTER\"\n",
-    "    model_examples.append(\n",
-    "        (\n",
-    "            e.inputs[\"query\"],\n",
-    "            {\"query\": e.outputs[\"output\"][\"query\"], \"filter\": string_filter},\n",
-    "        )\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "84593135",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever1 = SelfQueryRetriever.from_llm(\n",
-    "    llm,\n",
-    "    vectorstore,\n",
-    "    document_content_description,\n",
-    "    metadata_field_info,\n",
-    "    verbose=True,\n",
-    "    chain_kwargs={\"examples\": model_examples},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "4ec9bb92",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain1 = (\n",
-    "    RunnablePassthrough.assign(info=(lambda x: x[\"question\"]) | retriever1) | generator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "64eb88e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1. \"Saving Private Ryan\" (1998) - Directed by Steven Spielberg, this war film follows a group of soldiers during World War II as they search for a missing paratrooper.\\n\\n2. \"The Matrix\" (1999) - Directed by the Wachowskis, this science fiction action film follows a computer hacker who discovers the truth about the reality he lives in.\\n\\n3. \"Lethal Weapon 4\" (1998) - Directed by Richard Donner, this action-comedy film follows two mismatched detectives as they investigate a Chinese immigrant smuggling ring.\\n\\n4. \"The Fifth Element\" (1997) - Directed by Luc Besson, this science fiction action film follows a cab driver who must protect a mysterious woman who holds the key to saving the world.\\n\\n5. \"The Rock\" (1996) - Directed by Michael Bay, this action thriller follows a group of rogue military men who take over Alcatraz and threaten to launch missiles at San Francisco.'"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain1.invoke(\n",
-    "    {\"question\": \"what are good action movies made before 2000 but after 1997?\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e1ee8b55",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/rag_fusion.ipynb

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

cookbook/rag_with_quantized_embeddings.ipynb

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

cookbook/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/self-discover.ipynb

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

cookbook/self_query_hotel_search.ipynb

@@ -1083,7 +1083,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_elasticsearch import ElasticsearchStore\n",
+    "from langchain_community.vectorstores import ElasticsearchStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"

cookbook/wikibase_agent.ipynb

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

docker/Makefile

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

docker/docker-compose.yml

@@ -1,10 +1,5 @@
 # docker-compose to make it easier to spin up integration tests.
 # Services should use NON standard ports to avoid collision with
-# any existing services that might be used for development.
-# ATTENTION: When adding a service below use a non-standard port
-# increment by one from the preceding port.
-# For credentials always use `langchain` and `langchain` for the
-# username and password. 
 version: "3"
 name: langchain-tests
 
@@ -20,38 +15,3 @@ services:
       - "6020:6379"
     volumes:
       - ./redis-volume:/data
-  graphdb:
-    image: graphdb
-    ports:
-      - "6021:7200"
-  mongo:
-    image: mongo:latest
-    container_name: mongo_container
-    ports:
-      - "6022:27017"
-    environment:
-      MONGO_INITDB_ROOT_USERNAME: langchain
-      MONGO_INITDB_ROOT_PASSWORD: langchain
-  postgres:
-    image: postgres:16
-    environment:
-      POSTGRES_DB: langchain
-      POSTGRES_USER: langchain
-      POSTGRES_PASSWORD: langchain
-    ports:
-      - "6023:5432"
-    command: |
-      postgres -c log_statement=all
-    healthcheck:
-      test:
-        [
-          "CMD-SHELL",
-          "psql postgresql://langchain:langchain@localhost/langchain --command 'SELECT 1;' || exit 1",
-        ]
-      interval: 5s
-      retries: 60
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-
-volumes:
-  postgres_data:

docker/graphdb/Dockerfile

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

docker/graphdb/config.ttl

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

docker/graphdb/graphdb_create.sh

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

docs/.gitignore

@@ -1 +0,0 @@
-/.quarto/

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 sorted(links.items()):
+        for doc_name, link in 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

@@ -1,9 +1,7 @@
 """Script for auto-generating api_reference.rst."""
-
 import importlib
 import inspect
 import os
-import sys
 import typing
 from enum import Enum
 from pathlib import Path
@@ -15,6 +13,7 @@ from pydantic import BaseModel
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
+
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
@@ -187,7 +186,7 @@ def _load_package_modules(
             modules_by_namespace[top_namespace] = _module_members
 
         except ImportError as e:
-            print(f"Error: Unable to import module '{namespace}' with error: {e}")  # noqa: T201
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")
 
     return modules_by_namespace
 
@@ -218,8 +217,8 @@ def _construct_doc(
 
     for module in namespaces:
         _members = members_by_namespace[module]
-        classes = [el for el in _members["classes_"] if el["is_public"]]
-        functions = [el for el in _members["functions"] if el["is_public"]]
+        classes = _members["classes_"]
+        functions = _members["functions"]
         if not (classes or functions):
             continue
         section = f":mod:`{package_namespace}.{module}`"
@@ -245,6 +244,9 @@ 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":
@@ -262,7 +264,7 @@ Classes
 """
 
         if functions:
-            _functions = [f["qualified_name"] for f in functions]
+            _functions = [f["qualified_name"] for f in functions if f["is_public"]]
             fstring = "\n    ".join(sorted(_functions))
             full_doc += f"""\
 Functions
@@ -320,54 +322,31 @@ def _package_dir(package_name: str = "langchain") -> Path:
 
 
 def _get_package_version(package_dir: Path) -> str:
-    """Return the version of the package."""
-    try:
-        with open(package_dir.parent / "pyproject.toml", "r") as f:
-            pyproject = toml.load(f)
-    except FileNotFoundError as e:
-        print(
-            f"pyproject.toml not found in {package_dir.parent}.\n"
-            "You are either attempting to build a directory which is not a package or "
-            "the package is missing a pyproject.toml file which should be added."
-            "Aborting the build."
-        )
-        exit(1)
+    with open(package_dir.parent / "pyproject.toml", "r") as f:
+        pyproject = toml.load(f)
     return pyproject["tool"]["poetry"]["version"]
 
 
-def _out_file_path(package_name: str) -> Path:
+def _out_file_path(package_name: str = "langchain") -> Path:
     """Return the path to the file containing the documentation."""
     return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
-def _doc_first_line(package_name: str) -> str:
+def _doc_first_line(package_name: str = "langchain") -> str:
     """Return the path to the file containing the documentation."""
     return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
 
 
-def main(dirs: Optional[list] = None) -> None:
+def main() -> None:
     """Generate the api_reference.rst file for each package."""
-    print("Starting to build API reference files.")
-    if not dirs:
-        dirs = [
-            dir_
-            for dir_ in os.listdir(ROOT_DIR / "libs")
-            if dir_ not in ("cli", "partners")
-        ]
-        dirs += os.listdir(ROOT_DIR / "libs" / "partners")
-    for dir_ in dirs:
-        # 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_)
+    for dir in os.listdir(ROOT_DIR / "libs"):
+        if dir in ("cli", "partners"):
             continue
         else:
-            print("Building package:", dir_)
-            _build_rst_file(package_name=dir_)
-    print("API reference files built.")
+            _build_rst_file(package_name=dir)
+    for dir in os.listdir(ROOT_DIR / "libs" / "partners"):
+        _build_rst_file(package_name=dir)
 
 
 if __name__ == "__main__":
-    dirs = sys.argv[1:] or None
-    main(dirs=dirs)
+    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/additional_resources/tutorials.mdx

@@ -25,7 +25,6 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 ⛓ [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
 
 ### Short Tutorials
-
 [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 
 [LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
@@ -34,14 +33,6 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 ⛓ [LangChain 101 Course](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb) by **Ivan Reznikov**
 
-### Code Alongs
-
-DataCamp has developed a [Become a Generative AI Developer series](https://www.datacamp.com/ai-code-alongs) featuring 9 free code-alongs, including ones on building chatbots using LangChain and the OpenAI and Pinecone APIs. When you start a code along, you are launched into a fully configured notebook environment with an expert-led video to guide you through the project.
-
-⛓ [Prompt Engineering with GPT & LangChain](https://www.datacamp.com/code-along/prompt-engineering-gpt-langchain)
-
-⛓ [Retrieval Augmented Generation with the OpenAI API & Pinecone]https://www.datacamp.com/code-along/retrieval-augmented-generation-openai-api-pinecone
-
 ##  Tutorials
 
 ### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)

docs/docs/contributing/documentation.mdx

@@ -3,68 +3,24 @@ sidebar_position: 3
 ---
 # Contribute Documentation
 
-LangChain documentation consists of two components:
+The docs directory contains Documentation and API Reference.
 
-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.
+Documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
 
-The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
+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 `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/).
+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.
 
-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.
+## Build Documentation Locally
 
 ### Install dependencies
 
-- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. [Download link](https://quarto.org/docs/download/).
-
-From the **monorepo root**, run the following command to install the dependencies:
-
-```bash
-poetry install --with lint,docs --no-root
-````
+- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus.
+- `poetry install` from the monorepo 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:
@@ -90,9 +46,10 @@ make api_docs_linkcheck
 
 ### Linting and Formatting
 
-The Main Documentation is linted from the **monorepo root**. To lint the main documentation, run the following from there:
+The docs are linted from the monorepo root. To lint the docs, run the following from there:
 
 ```bash
+poetry install --with lint,typing
 make lint
 ```
 
@@ -100,73 +57,9 @@ If you have formatting-related errors, you can fix them automatically with:
 
 ```bash
 make format
-```
+``` 
 
-## ⌨️ 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
+## 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

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

docs/docs/contributing/repo_structure.mdx

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

docs/docs/expression_language/cookbook/agent.ipynb

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

docs/docs/expression_language/how_to/routing.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "raw",
+   "cell_type": "markdown",
    "id": "9e45e81c-e16e-4c6c-b6a3-2362e5193827",
    "metadata": {},
    "source": [
@@ -25,42 +25,53 @@
     "\n",
     "There are two ways to perform routing:\n",
     "\n",
-    "1. Conditionally return runnables from a [`RunnableLambda`](./functions) (recommended)\n",
-    "2. Using a `RunnableBranch`.\n",
+    "1. Using a `RunnableBranch`.\n",
+    "2. Writing custom factory function that takes the input of a previous step and returns a **runnable**. Importantly, this should return a **runnable** and NOT actually execute.\n",
     "\n",
     "We'll illustrate both methods using a two step sequence where the first step classifies an input question as being about `LangChain`, `Anthropic`, or `Other`, then routes to a corresponding prompt chain."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c1c6edac",
+   "id": "f885113d",
+   "metadata": {},
+   "source": [
+    "## Using a RunnableBranch\n",
+    "\n",
+    "A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n",
+    "\n",
+    "If no provided conditions match, it runs the default runnable.\n",
+    "\n",
+    "Here's an example of what it looks like in action:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1aa13c1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_core.output_parsers import StrOutputParser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed84c59a",
    "metadata": {},
    "source": [
-    "## Example Setup\n",
     "First, let's create a chain that will identify incoming questions as being about `LangChain`, `Anthropic`, or `Other`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8a8a1967",
+   "execution_count": 2,
+   "id": "3ec03886",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Anthropic'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatAnthropic\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
     "chain = (\n",
     "    PromptTemplate.from_template(\n",
     "        \"\"\"Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.\n",
@@ -75,14 +86,33 @@
     "    )\n",
     "    | ChatAnthropic()\n",
     "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "87ae7c1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Anthropic'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "chain.invoke({\"question\": \"how do I call Anthropic?\"})"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7655555f",
+   "id": "8aa0a365",
    "metadata": {},
    "source": [
     "Now, let's create three sub chains:"
@@ -90,8 +120,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "89d7722d",
+   "execution_count": 4,
+   "id": "d479962a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,12 +158,101 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "593eab06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableBranch\n",
+    "\n",
+    "branch = RunnableBranch(\n",
+    "    (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
+    "    (lambda x: \"langchain\" in x[\"topic\"].lower(), langchain_chain),\n",
+    "    general_chain,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "752c732e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "29231bb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" As Dario Amodei told me, here are some ways to use Anthropic:\\n\\n- Sign up for an account on Anthropic's website to access tools like Claude, Constitutional AI, and Writer. \\n\\n- Use Claude for tasks like email generation, customer service chat, and QA. Claude can understand natural language prompts and provide helpful responses.\\n\\n- Use Constitutional AI if you need an AI assistant that is harmless, honest, and helpful. It is designed to be safe and aligned with human values.\\n\\n- Use Writer to generate natural language content for things like marketing copy, stories, reports, and more. Give it a topic and prompt and it will create high-quality written content.\\n\\n- Check out Anthropic's documentation and blog for tips, tutorials, examples, and announcements about new capabilities as they continue to develop their AI technology.\\n\\n- Follow Anthropic on social media or subscribe to their newsletter to stay up to date on new features and releases.\\n\\n- For most people, the easiest way to leverage Anthropic's technology is through their website - just create an account to get started!\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c67d8733",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' As Harrison Chase told me, here is how you use LangChain:\\n\\nLangChain is an AI assistant that can have conversations, answer questions, and generate text. To use LangChain, you simply type or speak your input and LangChain will respond. \\n\\nYou can ask LangChain questions, have discussions, get summaries or explanations about topics, and request it to generate text on a subject. Some examples of interactions:\\n\\n- Ask general knowledge questions and LangChain will try to answer factually. For example \"What is the capital of France?\"\\n\\n- Have conversations on topics by taking turns speaking. You can prompt the start of a conversation by saying something like \"Let\\'s discuss machine learning\"\\n\\n- Ask for summaries or high-level explanations on subjects. For example \"Can you summarize the main themes in Shakespeare\\'s Hamlet?\" \\n\\n- Give creative writing prompts or requests to have LangChain generate text in different styles. For example \"Write a short children\\'s story about a mouse\" or \"Generate a poem in the style of Robert Frost about nature\"\\n\\n- Correct LangChain if it makes an inaccurate statement and provide the right information. This helps train it.\\n\\nThe key is interacting naturally and giving it clear prompts and requests', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "935ad949",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' 2 + 2 = 4', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_chain.invoke({\"question\": \"whats 2 + 2\"})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6d8d042c",
    "metadata": {},
    "source": [
-    "## Using a custom function (Recommended)\n",
+    "## Using a custom function\n",
     "\n",
     "You can also use a custom function to route between different outputs. Here's an example:"
    ]
@@ -231,89 +350,13 @@
     "full_chain.invoke({\"question\": \"whats 2 + 2\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5147b827",
-   "metadata": {},
-   "source": [
-    "## Using a RunnableBranch\n",
-    "\n",
-    "A `RunnableBranch` is a special type of runnable that allows you to define a set of conditions and runnables to execute based on the input. It does **not** offer anything that you can't achieve in a custom function as described above, so we recommend using a custom function instead.\n",
-    "\n",
-    "A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n",
-    "\n",
-    "If no provided conditions match, it runs the default runnable.\n",
-    "\n",
-    "Here's an example of what it looks like in action:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2a101418",
+   "id": "46802d04",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" As Dario Amodei told me, here are some ways to use Anthropic:\\n\\n- Sign up for an account on Anthropic's website to access tools like Claude, Constitutional AI, and Writer. \\n\\n- Use Claude for tasks like email generation, customer service chat, and QA. Claude can understand natural language prompts and provide helpful responses.\\n\\n- Use Constitutional AI if you need an AI assistant that is harmless, honest, and helpful. It is designed to be safe and aligned with human values.\\n\\n- Use Writer to generate natural language content for things like marketing copy, stories, reports, and more. Give it a topic and prompt and it will create high-quality written content.\\n\\n- Check out Anthropic's documentation and blog for tips, tutorials, examples, and announcements about new capabilities as they continue to develop their AI technology.\\n\\n- Follow Anthropic on social media or subscribe to their newsletter to stay up to date on new features and releases.\\n\\n- For most people, the easiest way to leverage Anthropic's technology is through their website - just create an account to get started!\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from langchain_core.runnables import RunnableBranch\n",
-    "\n",
-    "branch = RunnableBranch(\n",
-    "    (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
-    "    (lambda x: \"langchain\" in x[\"topic\"].lower(), langchain_chain),\n",
-    "    general_chain,\n",
-    ")\n",
-    "full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | branch\n",
-    "full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8d8caf9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' As Harrison Chase told me, here is how you use LangChain:\\n\\nLangChain is an AI assistant that can have conversations, answer questions, and generate text. To use LangChain, you simply type or speak your input and LangChain will respond. \\n\\nYou can ask LangChain questions, have discussions, get summaries or explanations about topics, and request it to generate text on a subject. Some examples of interactions:\\n\\n- Ask general knowledge questions and LangChain will try to answer factually. For example \"What is the capital of France?\"\\n\\n- Have conversations on topics by taking turns speaking. You can prompt the start of a conversation by saying something like \"Let\\'s discuss machine learning\"\\n\\n- Ask for summaries or high-level explanations on subjects. For example \"Can you summarize the main themes in Shakespeare\\'s Hamlet?\" \\n\\n- Give creative writing prompts or requests to have LangChain generate text in different styles. For example \"Write a short children\\'s story about a mouse\" or \"Generate a poem in the style of Robert Frost about nature\"\\n\\n- Correct LangChain if it makes an inaccurate statement and provide the right information. This helps train it.\\n\\nThe key is interacting naturally and giving it clear prompts and requests', additional_kwargs={}, example=False)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26159af7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' 2 + 2 = 4', additional_kwargs={}, example=False)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "full_chain.invoke({\"question\": \"whats 2 + 2\"})"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

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_community.chat_models import ChatAnthropic\n",
+    "from langchain.chat_models import ChatAnthropic\n",
     "\n",
     "model = ChatAnthropic()\n",
     "\n",
@@ -464,12 +464,12 @@
    "id": "6fd3e71b-439e-418f-8a8a-5232fba3d9fd",
    "metadata": {},
    "source": [
-    "Stream just yielded the final result from that component.\n",
+    "Stream just yielded the final result from that component. \n",
     "\n",
     "This is OK 🥹! Not all components have to implement streaming -- in some cases streaming is either unnecessary, difficult or just doesn't make sense.\n",
     "\n",
     ":::{.callout-tip}\n",
-    "An LCEL chain constructed using non-streaming components, will still be able to stream in a lot of cases, with streaming of partial output starting after the last non-streaming step in the chain.\n",
+    "An LCEL chain constructed using using non-streaming components, will still be able to stream in a lot of cases, with streaming of partial output starting after the last non-streaming step in the chain.\n",
     ":::"
    ]
   },

docs/docs/get_started/quickstart.mdx

@@ -58,17 +58,17 @@ 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 to 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 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.
 
 ## LLM Chain
 
-We'll show how to use models available via API, like OpenAI and Cohere, and local open source models, using integrations like Ollama.
+For this getting started guide, we will provide two options: using OpenAI (a popular model available via API) or using a local open source model.
 
 <Tabs>
-  <TabItem value="openai" label="OpenAI (API)" default>
+  <TabItem value="openai" label="OpenAI" default>
 
 First we'll need to import the LangChain x OpenAI integration package.
 
@@ -99,7 +99,7 @@ llm = ChatOpenAI(openai_api_key="...")
 ```
 
   </TabItem>
-  <TabItem value="local" label="Local (using Ollama)">
+  <TabItem value="local" label="Local">
 
 [Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.
 
@@ -112,37 +112,6 @@ Then, make sure the Ollama server is running. After that, you can do:
 ```python
 from langchain_community.llms import Ollama
 llm = Ollama(model="llama2")
-```
-
-  </TabItem>
-  <TabItem value="cohere" label="Cohere (API)" default>
-
-First we'll need to import the Cohere SDK package.
-
-```shell
-pip install cohere
-```
-
-Accessing the API requires an API key, which you can get by creating an account and heading [here](https://dashboard.cohere.com/api-keys). Once we have a key we'll want to set it as an environment variable by running:
-
-```shell
-export COHERE_API_KEY="..."
-```
-
-We can then initialize the model:
-
-```python
-from langchain_community.chat_models import ChatCohere
-
-llm = ChatCohere()
-```
-
-If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
-
-```python
-from langchain_community.chat_models import ChatCohere
-
-llm = ChatCohere(cohere_api_key="...")
 ```
 
   </TabItem>
@@ -224,17 +193,17 @@ After that, we can import and use WebBaseLoader.
 
 ```python
 from langchain_community.document_loaders import WebBaseLoader
-loader = WebBaseLoader("https://docs.smith.langchain.com")
+loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
 
 docs = loader.load()
 ```
 
 Next, we need to index it into a vectorstore. This requires a few components, namely an [embedding model](/docs/modules/data_connection/text_embedding) and a [vectorstore](/docs/modules/data_connection/vectorstores).
 
-For embedding models, we once again provide examples for accessing via API or by running local models.
+For embedding models, we once again provide examples for accessing via OpenAI or via local models.
 
 <Tabs>
-  <TabItem value="openai" label="OpenAI (API)" default>
+  <TabItem value="openai" label="OpenAI" default>
   
 Make sure you have the `langchain_openai` package installed an the appropriate environment variables set (these are the same as needed for the LLM).
 
@@ -245,7 +214,7 @@ embeddings = OpenAIEmbeddings()
 ```
 
 </TabItem>
-<TabItem value="local" label="Local (using Ollama)">
+<TabItem value="local" label="Local">
 
 Make sure you have Ollama running (same set up as with the LLM).
 
@@ -255,17 +224,6 @@ from langchain_community.embeddings import OllamaEmbeddings
 embeddings = OllamaEmbeddings()
 ```
   </TabItem>
-<TabItem value="cohere" label="Cohere (API)" default>
-
-Make sure you have the `cohere` package installed an the appropriate environment variables set (these are the same as needed for the LLM).
-
-```python
-from langchain_community.embeddings import CohereEmbeddings
-
-embeddings = CohereEmbeddings()
-```
-
-</TabItem>
 </Tabs>
 
 Now, we can use this embedding model to ingest documents into a vectorstore.
@@ -416,7 +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 to two tools:
+For this example, we will give the agent access 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_core.agents import AgentAction\n",
+    "from langchain.schema 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_core.documents import Document\n",
+    "from langchain.schema 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_core.prompts import format_document\n",
+    "from langchain.schema import format_document\n",
     "\n",
     "DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
     "\n",

docs/docs/guides/structured_output.ipynb

@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6e3f0f72",
-   "metadata": {},
-   "source": [
-    "# [beta] Structured Output\n",
-    "\n",
-    "It is often crucial to have LLMs return structured output. This is because often times the outputs of the LLMs are used in downstream applications, where specific arguments are required. Having the LLM return structured output reliably is necessary for that.\n",
-    "\n",
-    "There are a few different high level strategies that are used to do this:\n",
-    "\n",
-    "- Prompting: This is when you ask the LLM (very nicely) to return output in the desired format (JSON, XML). This is nice because works with all LLMs, this is not nice because it doesn't garuntee that the LLM returns in the right format.\n",
-    "- Function calling: This is when the LLM is finetuned to be able to not just generate a completion, but also generate a function call. The functions the LLM can call are generally passed as extra parameters to the model API. The function names and descriptions should be treated as part of the prompt (they usually count against token counts, and are used by the LLM to decide what to do).\n",
-    "- Tool calling: A technique similar to function calling, but it allows the LLM to call multiple functions at the same time.\n",
-    "- JSON mode: This is when the LLM is garunteed to return JSON.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Different models may support different variants of these, with slightly different parameters. In order to make it easy to get LLMs to return structured output, we have added a common interface to LangChain models: `.with_structured_output`. \n",
-    "\n",
-    "By invoking this method (and passing in a JSON schema or a Pydantic model) the model will add whatever model parameters + output parsers are necessary to get back the structured output. There may be more than one way to do this (eg function calling vs JSON mode) - you can configure which method to use by passing into that method.\n",
-    "\n",
-    "Let's look at some examples of this in action!\n",
-    "\n",
-    "We will use Pydantic to easily structure the response schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "08029f4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "070bf702",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Joke(BaseModel):\n",
-    "    setup: str = Field(description=\"The setup of the joke\")\n",
-    "    punchline: str = Field(description=\"The punchline to the joke\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98f6edfa",
-   "metadata": {},
-   "source": [
-    "## OpenAI\n",
-    "\n",
-    "OpenAI exposes a few different ways to get structured outputs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3fe7caf0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "deddb6d3",
-   "metadata": {},
-   "source": [
-    "### Function Calling\n",
-    "\n",
-    "By default, we will use `function_calling`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "6700994a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatOpenAI()\n",
-    "model_with_structure = model.with_structured_output(Joke)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c55a61b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Joke(setup='Why was the cat sitting on the computer?', punchline='It wanted to keep an eye on the mouse!')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_structure.invoke(\"Tell me a joke about cats\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39d7a555",
-   "metadata": {},
-   "source": [
-    "### JSON Mode\n",
-    "\n",
-    "We also support JSON mode. Note that we need to specify in the prompt the format that it should respond in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "df0370e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_with_structure = model.with_structured_output(Joke, method=\"json_mode\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "23844a26",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Joke(setup=\"Why don't cats play poker in the jungle?\", punchline='Too many cheetahs!')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_structure.invoke(\n",
-    "    \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f3cce9e",
-   "metadata": {},
-   "source": [
-    "## Fireworks\n",
-    "\n",
-    "[Fireworks](https://fireworks.ai/) similarly supports function calling and JSON mode for select models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ad45fdd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_fireworks import ChatFireworks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36270ed5",
-   "metadata": {},
-   "source": [
-    "### Function Calling\n",
-    "\n",
-    "By default, we will use `function_calling`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "49a20847",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v1\")\n",
-    "model_with_structure = model.with_structured_output(Joke)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e3093a6c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Joke(setup=\"Why don't cats play poker in the jungle?\", punchline='Too many cheetahs!')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_structure.invoke(\"Tell me a joke about cats\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddb6b3ba",
-   "metadata": {},
-   "source": [
-    "### JSON Mode\n",
-    "\n",
-    "We also support JSON mode. Note that we need to specify in the prompt the format that it should respond in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ea0c22c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_with_structure = model.with_structured_output(Joke, method=\"json_mode\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "649f9632",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Joke(setup='Why did the dog sit in the shade?', punchline='To avoid getting burned.')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_structure.invoke(\n",
-    "    \"Tell me a joke about dogs, respond in JSON with `setup` and `punchline` keys\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff70609a",
-   "metadata": {},
-   "source": [
-    "## Mistral\n",
-    "\n",
-    "We also support structured output with Mistral models, although we only support function calling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bffd3fad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_mistralai import ChatMistralAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c8bd7549",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatMistralAI(model=\"mistral-large-latest\")\n",
-    "model_with_structure = model.with_structured_output(Joke)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17b15816",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_with_structure.invoke(\"Tell me a joke about cats\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bbbb698",
-   "metadata": {},
-   "source": [
-    "## Together\n",
-    "\n",
-    "Since [TogetherAI](https://www.together.ai/) is just a drop in replacement for OpenAI, we can just use the OpenAI integration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "9b9617e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "90549664",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatOpenAI(\n",
-    "    base_url=\"https://api.together.xyz/v1\",\n",
-    "    api_key=os.environ[\"TOGETHER_API_KEY\"],\n",
-    "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
-    ")\n",
-    "model_with_structure = model.with_structured_output(Joke)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "01da39be",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Joke(setup='Why did the cat sit on the computer?', punchline='To keep an eye on the mouse!')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_structure.invoke(\"Tell me a joke about cats\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3066b2af",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/callbacks/fiddler.ipynb

@@ -1,215 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0cebf93b",
-   "metadata": {},
-   "source": [
-    "## Fiddler Langchain integration Quick Start Guide\n",
-    "\n",
-    "Fiddler is the pioneer in enterprise Generative and Predictive system ops, offering a unified platform that enables Data Science, MLOps, Risk, Compliance, Analytics, and other LOB teams to monitor, explain, analyze, and improve ML deployments at enterprise scale. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38d746c2",
-   "metadata": {},
-   "source": [
-    "## 1. Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e0151955",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# langchain langchain-community langchain-openai fiddler-client"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5662f2e5-d510-4eef-b44b-fa929e5b4ad4",
-   "metadata": {},
-   "source": [
-    "## 2. Fiddler connection details "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64fac323",
-   "metadata": {},
-   "source": [
-    "*Before you can add information about your model with Fiddler*\n",
-    "\n",
-    "1. The URL you're using to connect to Fiddler\n",
-    "2. Your organization ID\n",
-    "3. Your authorization token\n",
-    "\n",
-    "These can be found by navigating to the *Settings* page of your Fiddler environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6f8b73e-d350-40f0-b7a4-fb1e68a65a22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "URL = \"\"  # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai\n",
-    "ORG_NAME = \"\"\n",
-    "AUTH_TOKEN = \"\"  # Your Fiddler instance auth token\n",
-    "\n",
-    "# Fiddler project and model names, used for model registration\n",
-    "PROJECT_NAME = \"\"\n",
-    "MODEL_NAME = \"\"  # Model name in Fiddler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0645805a",
-   "metadata": {},
-   "source": [
-    "## 3. Create a fiddler callback handler instance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13de4f9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler\n",
-    "\n",
-    "fiddler_handler = FiddlerCallbackHandler(\n",
-    "    url=URL,\n",
-    "    org=ORG_NAME,\n",
-    "    project=PROJECT_NAME,\n",
-    "    model=MODEL_NAME,\n",
-    "    api_key=AUTH_TOKEN,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2276368e-f1dc-46be-afe3-18796e7a66f2",
-   "metadata": {},
-   "source": [
-    "## Example 1 : Basic Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9de0fd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_openai import OpenAI\n",
-    "\n",
-    "# Note : Make sure openai API key is set in the environment variable OPENAI_API_KEY\n",
-    "llm = OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler])\n",
-    "output_parser = StrOutputParser()\n",
-    "\n",
-    "chain = llm | output_parser\n",
-    "\n",
-    "# Invoke the chain. Invocation will be logged to Fiddler, and metrics automatically generated\n",
-    "chain.invoke(\"How far is moon from earth?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "309bde0b-e1ce-446c-98ac-3690c26a2676",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Few more invocations\n",
-    "chain.invoke(\"What is the temperature on Mars?\")\n",
-    "chain.invoke(\"How much is 2 + 200000?\")\n",
-    "chain.invoke(\"Which movie won the oscars this year?\")\n",
-    "chain.invoke(\"Can you write me a poem about insomnia?\")\n",
-    "chain.invoke(\"How are you doing today?\")\n",
-    "chain.invoke(\"What is the meaning of life?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48fa4782-c867-4510-9430-4ffa3de3b5eb",
-   "metadata": {},
-   "source": [
-    "## Example 2 : Chain with prompt templates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2aa2c220-8946-4844-8d3c-8f69d744d13f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import (\n",
-    "    ChatPromptTemplate,\n",
-    "    FewShotChatMessagePromptTemplate,\n",
-    ")\n",
-    "\n",
-    "examples = [\n",
-    "    {\"input\": \"2+2\", \"output\": \"4\"},\n",
-    "    {\"input\": \"2+3\", \"output\": \"5\"},\n",
-    "]\n",
-    "\n",
-    "example_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        (\"ai\", \"{output}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "few_shot_prompt = FewShotChatMessagePromptTemplate(\n",
-    "    example_prompt=example_prompt,\n",
-    "    examples=examples,\n",
-    ")\n",
-    "\n",
-    "final_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a wondrous wizard of math.\"),\n",
-    "        few_shot_prompt,\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# Note : Make sure openai API key is set in the environment variable OPENAI_API_KEY\n",
-    "llm = OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler])\n",
-    "\n",
-    "chain = final_prompt | llm\n",
-    "\n",
-    "# Invoke the chain. Invocation will be logged to Fiddler, and metrics automatically generated\n",
-    "chain.invoke({\"input\": \"What's the square of a triangle?\"})"
-   ]
-  }
- ],
- "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": "3.9.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/callbacks/labelstudio.ipynb

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

docs/docs/integrations/chat/ai21.ipynb

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

docs/docs/integrations/chat/anthropic.ipynb

@@ -17,44 +17,40 @@
    "source": [
     "# ChatAnthropic\n",
     "\n",
-    "This notebook covers how to get started with Anthropic chat models.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "For setup instructions, please see the Installation and Environment Setup sections of the [Anthropic Platform page](/docs/integrations/platforms/anthropic.mdx)."
+    "This notebook covers how to get started with Anthropic chat models."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "91be2e12",
-   "metadata": {},
+   "execution_count": 1,
+   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-01-19T11:25:00.590587Z",
+     "start_time": "2024-01-19T11:25:00.127293Z"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-anthropic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "584ed5ec",
-   "metadata": {},
-   "source": [
-    "## Environment Setup\n",
-    "\n",
-    "We'll need to get a [Anthropic](https://console.anthropic.com/settings/keys) and set the `ANTHROPIC_API_KEY` environment variable:"
+    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "01578ae3",
-   "metadata": {},
+   "execution_count": 2,
+   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-01-19T11:25:04.349676Z",
+     "start_time": "2024-01-19T11:25:03.964930Z"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")"
    ]
   },
   {
@@ -86,9 +82,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AIMessage(content=' 저는 파이썬을 좋아합니다.')"
-      ]
+      "text/plain": "AIMessage(content=' 저는 파이썬을 좋아합니다.')"
      },
      "execution_count": 3,
      "metadata": {},
@@ -96,11 +90,6 @@
     }
    ],
    "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "chat = ChatAnthropic(temperature=0, model_name=\"claude-2\")\n",
-    "\n",
     "system = (\n",
     "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
     ")\n",
@@ -139,9 +128,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "AIMessage(content=\" Why don't bears like fast food? Because they can't catch it!\")"
-      ]
+      "text/plain": "AIMessage(content=\" Why don't bears like fast food? Because they can't catch it!\")"
      },
      "execution_count": 4,
      "metadata": {},
@@ -202,6 +189,154 @@
     "for chunk in chain.stream({}):\n",
     "    print(chunk.content, end=\"\", flush=True)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3737fc8d",
+   "metadata": {},
+   "source": [
+    "# ChatAnthropicMessages\n",
+    "\n",
+    "LangChain also offers the beta Anthropic Messages endpoint through the new `langchain-anthropic` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c253883f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-anthropic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "07c47c2a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-01-19T11:25:25.288133Z",
+     "start_time": "2024-01-19T11:25:24.438968Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropicMessages\n",
+    "\n",
+    "chat = ChatAnthropicMessages(model_name=\"claude-instant-1.2\")\n",
+    "system = (\n",
+    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    ")\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Korean\",\n",
+    "        \"text\": \"I love Python\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e53d75935143fd",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "ChatAnthropicMessages also requires the anthropic_api_key argument, or the ANTHROPIC_API_KEY environment variable must be set. \n",
+    "\n",
+    "ChatAnthropicMessages also supports async and streaming functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e20a139d30e3d333",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-01-19T11:25:26.012325Z",
+     "start_time": "2024-01-19T11:25:25.288358Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "AIMessage(content='파이썬을 사랑합니다.')"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chain.ainvoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Korean\",\n",
+    "        \"text\": \"I love Python\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f34f1073d7e7120",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-01-19T11:25:28.323455Z",
+     "start_time": "2024-01-19T11:25:26.012040Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are some of the most famous tourist attractions in Japan:\n",
+      "\n",
+      "- Tokyo Tower - A communication and observation tower in Tokyo modeled after the Eiffel Tower. It offers stunning views of the city.\n",
+      "\n",
+      "- Mount Fuji - Japan's highest and most famous mountain. It's a iconic symbol of Japan and a UNESCO World Heritage Site. \n",
+      "\n",
+      "- Itsukushima Shrine (Miyajima) - A shrine located on an island in Hiroshima prefecture, known for its \"floating\" torii gate that seems to float on water during high tide.\n",
+      "\n",
+      "- Himeji Castle - A UNESCO World Heritage Site famous for having withstood numerous battles without destruction to its intricate white walls and sloping, triangular roofs. \n",
+      "\n",
+      "- Kawaguchiko Station - Near Mount Fuji, this area is known for its scenic Fuji Five Lakes region. \n",
+      "\n",
+      "- Hiroshima Peace Memorial Park and Museum - Commemorates the world's first atomic bombing in Hiroshima on August 6, 1945. \n",
+      "\n",
+      "- Arashiyama Bamboo Grove - A renowned bamboo forest located in Kyoto that draws many visitors.\n",
+      "\n",
+      "- Kegon Falls - One of Japan's largest waterfalls"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"human\", \"Give me a list of famous tourist attractions in Japan\")]\n",
+    ")\n",
+    "chain = prompt | chat\n",
+    "for chunk in chain.stream({}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -15,7 +15,16 @@
    "execution_count": 1,
    "id": "378be79b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_experimental.llms.anthropic_functions import AnthropicFunctions"
    ]
@@ -32,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e1d535f6",
    "metadata": {},
    "outputs": [],
@@ -83,7 +92,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.messages import HumanMessage"
+    "from langchain.schema import HumanMessage"
    ]
   },
   {
@@ -93,7 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = model.invoke(\n",
+    "response = model.predict_messages(\n",
     "    [HumanMessage(content=\"whats the weater in boston?\")], functions=functions\n",
     ")"
    ]
@@ -131,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "7af5c567",
    "metadata": {},
    "outputs": [],
@@ -153,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "bd01082a",
    "metadata": {},
    "outputs": [],
@@ -163,12 +172,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b5a23e9f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
+       " {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "chain.invoke(inp)"
+    "chain.run(inp)"
    ]
   },
   {
@@ -235,7 +256,7 @@
     }
    ],
    "source": [
-    "chain.invoke(\"this is really cool\")"
+    "chain.run(\"this is really cool\")"
    ]
   }
  ],
@@ -255,7 +276,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/anyscale.ipynb

@@ -109,7 +109,7 @@
    "source": [
     "import asyncio\n",
     "\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain.schema 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_core.messages import HumanMessage\n",
+    "from langchain.schema 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_community.chat_models import ChatBaichuan\n",
-    "from langchain_core.messages import HumanMessage"
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_community.chat_models import ChatBaichuan"
    ]
   },
   {

docs/docs/integrations/chat/bedrock.ipynb

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

docs/docs/integrations/chat/deepinfra.ipynb

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

@@ -23,14 +23,6 @@
     "This example goes over how to use LangChain to interact with `ChatFireworks` models."
    ]
   },
-  {
-   "cell_type": "raw",
-   "id": "4a7c795e",
-   "metadata": {},
-   "source": [
-    "%pip install langchain-fireworks"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -43,8 +35,10 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "from langchain_fireworks import ChatFireworks"
+    "import os\n",
+    "\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_community.chat_models.fireworks import ChatFireworks"
    ]
   },
   {
@@ -54,7 +48,7 @@
    "source": [
     "# Setup\n",
     "\n",
-    "1. Make sure the `langchain-fireworks` package is installed in your environment.\n",
+    "1. Make sure the `fireworks-ai` package is installed in your environment.\n",
     "2. Sign in to [Fireworks AI](http://fireworks.ai) for the an API Key to access our models, and make sure it is set as the `FIREWORKS_API_KEY` environment variable.\n",
     "3. Set up your model using a model id. If the model is not set, the default model is fireworks-llama-v2-7b-chat. See the full, most up-to-date model list on [app.fireworks.ai](https://app.fireworks.ai)."
    ]
@@ -73,7 +67,7 @@
     "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")\n",
     "\n",
     "# Initialize a Fireworks chat model\n",
-    "chat = ChatFireworks(model=\"accounts/fireworks/models/mixtral-8x7b-instruct\")"
+    "chat = ChatFireworks(model=\"accounts/fireworks/models/llama-v2-13b-chat\")"
    ]
   },
   {
@@ -88,17 +82,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "72340871-ae2f-415f-b399-0777d32dc379",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Hello! I'm an AI language model, a helpful assistant designed to chat and assist you with any questions or information you might need. I'm here to make your experience as smooth and enjoyable as possible. How can I assist you today?\")"
+       "AIMessage(content=\"Hello! My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. My primary function is to assist and converse with users like you, answering questions and engaging in discussion to the best of my ability. I'm here to help and provide information on a wide range of topics, so feel free to ask me anything!\", additional_kwargs={}, example=False)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -108,22 +102,22 @@
     "system_message = SystemMessage(content=\"You are to chat with the user.\")\n",
     "human_message = HumanMessage(content=\"Who are you?\")\n",
     "\n",
-    "chat.invoke([system_message, human_message])"
+    "chat([system_message, human_message])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "68c6b1fa-2ff7-4a63-8d88-3cec302180b8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm an AI and do not have the ability to experience the weather firsthand. However,\")"
+       "AIMessage(content=\"Oh hello there! *giggle* It's such a beautiful day today, isn\", additional_kwargs={}, example=False)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,70 +125,200 @@
    "source": [
     "# Setting additional parameters: temperature, max_tokens, top_p\n",
     "chat = ChatFireworks(\n",
-    "    model=\"accounts/fireworks/models/mixtral-8x7b-instruct\",\n",
-    "    temperature=1,\n",
-    "    max_tokens=20,\n",
+    "    model=\"accounts/fireworks/models/llama-v2-13b-chat\",\n",
+    "    model_kwargs={\"temperature\": 1, \"max_tokens\": 20, \"top_p\": 1},\n",
     ")\n",
     "system_message = SystemMessage(content=\"You are to chat with the user.\")\n",
     "human_message = HumanMessage(content=\"How's the weather today?\")\n",
-    "chat.invoke([system_message, human_message])"
+    "chat([system_message, human_message])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8c44cb36",
+   "id": "d93aa186-39cf-4e1a-aa32-01ed31d43bc8",
    "metadata": {},
    "source": [
-    "# Tool Calling\n",
+    "# Simple Chat Chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28763fbc",
+   "metadata": {},
+   "source": [
+    "You can use chat models on fireworks, with system prompts and memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cbe29efc-37c3-4c83-8b84-b8bba1a1e589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain_community.chat_models import ChatFireworks\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
-    "Fireworks offers the [`FireFunction-v1` tool calling model](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling). You can use it for structured output and function calling use cases:"
+    "llm = ChatFireworks(\n",
+    "    model=\"accounts/fireworks/models/llama-v2-13b-chat\",\n",
+    "    model_kwargs={\"temperature\": 0, \"max_tokens\": 64, \"top_p\": 1.0},\n",
+    ")\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a helpful chatbot that speaks like a pirate.\"),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02991e05-a38e-47d4-9ab3-7e630a8ead55",
+   "metadata": {},
+   "source": [
+    "Initially, there is no chat memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e2fd186f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'history': []}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory = ConversationBufferMemory(return_messages=True)\n",
+    "memory.load_memory_variables({})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bee461da",
+   "metadata": {},
+   "source": [
+    "Create a simple chain with memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86972e54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = (\n",
+    "    RunnablePassthrough.assign(\n",
+    "        history=memory.load_memory_variables | (lambda x: x[\"history\"])\n",
+    "    )\n",
+    "    | prompt\n",
+    "    | llm.bind(stop=[\"\\n\\n\"])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f48cb142",
+   "metadata": {},
+   "source": [
+    "Run the chain with a simple question, expecting an answer aligned with the system message provided."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "db3ad5b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Ahoy there, me hearty! Yer a fine lookin' swashbuckler, I can see that! *adjusts eye patch* What be bringin' ye to these waters? Are ye here to plunder some booty or just to enjoy the sea breeze?\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"input\": \"hi im bob\"}\n",
+    "response = chain.invoke(inputs)\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "338f4bae",
+   "metadata": {},
+   "source": [
+    "Save the memory context, then read it back to inspect contents"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "ee2db682",
+   "id": "257eec01",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'function': {'arguments': '{\"name\": \"Erick\", \"age\": 27}',\n",
-      "              'name': 'ExtractFields'},\n",
-      " 'id': 'call_J0WYP2TLenaFw3UeVU0UnWqx',\n",
-      " 'index': 0,\n",
-      " 'type': 'function'}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'history': [HumanMessage(content='hi im bob', additional_kwargs={}, example=False),\n",
+       "  AIMessage(content=\"Ahoy there, me hearty! Yer a fine lookin' swashbuckler, I can see that! *adjusts eye patch* What be bringin' ye to these waters? Are ye here to plunder some booty or just to enjoy the sea breeze?\", additional_kwargs={}, example=False)]}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from pprint import pprint\n",
-    "\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
-    "\n",
-    "\n",
-    "class ExtractFields(BaseModel):\n",
-    "    name: str\n",
-    "    age: int\n",
-    "\n",
-    "\n",
-    "chat = ChatFireworks(\n",
-    "    model=\"accounts/fireworks/models/firefunction-v1\",\n",
-    ").bind_tools([ExtractFields])\n",
-    "\n",
-    "result = chat.invoke(\"I am a 27 year old named Erick\")\n",
-    "\n",
-    "pprint(result.additional_kwargs[\"tool_calls\"][0])"
+    "memory.save_context(inputs, {\"output\": response.content})\n",
+    "memory.load_memory_variables({})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08441347",
+   "metadata": {},
+   "source": [
+    "Now as another question that requires use of the memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "2321a4e6",
+   "execution_count": 12,
+   "id": "7f5f2820",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Arrrr, ye be askin' about yer name, eh? Well, me matey, I be knowin' ye as Bob, the scurvy dog! *winks* But if ye want me to call ye somethin' else, just let me know, and I\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"input\": \"whats my name\"}\n",
+    "chain.invoke(inputs)"
+   ]
   }
  ],
  "metadata": {
@@ -213,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/gigachat.ipynb

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

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -320,51 +320,20 @@
     "4. Message may be blocked if they violate the safety checks of the LLM. In this case, the model will return an empty response."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "54793b9e",
-   "metadata": {},
-   "source": [
-    "### Safety Settings\n",
-    "\n",
-    "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "75fdfad6",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain_google_genai import (\n",
-    "    ChatGoogleGenerativeAI,\n",
-    "    HarmBlockThreshold,\n",
-    "    HarmCategory,\n",
-    ")\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-pro\",\n",
-    "    safety_settings={\n",
-    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e68e203d",
-   "metadata": {},
-   "source": [
-    "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
    "id": "92b5aca5",
    "metadata": {},
    "source": [
-    "## Additional Configuration\n",
+    "## Additional Configuraation\n",
     "\n",
     "You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:\n",
     "\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\n",
-    "from langchain_core.messages import HumanMessage"
+    "from langchain_community.chat_models.gpt_router import GPTRouterModel"
    ]
   },
   {

docs/docs/integrations/chat/groq.ipynb

@@ -1,179 +0,0 @@
-{
- "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({\"text\": \"Explain the importance of low latency LLMs.\"})"
-   ]
-  },
-  {
-   "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_community.chat_models import JinaChat\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage"
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_community.chat_models import JinaChat"
    ]
   },
   {

docs/docs/integrations/chat/kinetica.ipynb

@@ -1,654 +0,0 @@
-{
- "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_community.chat_models import ChatKonko\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage"
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain_community.chat_models import ChatKonko"
    ]
   },
   {

docs/docs/integrations/chat/litellm.ipynb

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

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_core.messages import SystemMessage\n",
+    "from langchain.schema 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_community.chat_models import MiniMaxChat\n",
-    "from langchain_core.messages import HumanMessage"
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_community.chat_models import MiniMaxChat"
    ]
   },
   {

docs/docs/integrations/chat/ollama.ipynb

@@ -278,7 +278,7 @@
     }
    ],
    "source": [
-    "from langchain_core.messages import HumanMessage\n",
+    "from langchain.schema 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_core.messages import HumanMessage\n",
+    "from langchain.schema 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_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "from langchain_openai import ChatOpenAI"
    ]
   },