docs: tool-use use case (#15783)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -5,60 +5,84 @@ body:
   - type: markdown
     attributes:
       value: >
-        Thank you for taking the time to file a bug report. Before creating a new
-        issue, please make sure to take a few moments to check the issue tracker
-        for existing issues about the bug.
+        Thank you for taking the time to file a bug report. 
+        
+        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
+        if there's another way to solve your problem:
+        
+        [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)
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: Checked other resources
+      description: Please confirm and check all the following options.
+      options:
+        - label: I added a very descriptive title to this issue.
+          required: true
+        - label: I searched the LangChain documentation with the integrated search.
+          required: true
+        - label: I used the GitHub search to find a similar question and didn't find it.
+          required: true
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Example Code
+      description: |
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        
+        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+        
+        If you're including an error message, please include the full stack trace not just the last error.
+        
+        **Important!** Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
 
+      placeholder: |
+        The following code: 
+        
+        ```python
+        from langchain_core.runnables import RunnableLambda
+
+        def bad_code(inputs) -> int:
+          raise NotImplementedError('For demo purpose')
+          
+          chain = RunnableLambda(bad_code)
+          chain.invoke('Hello!')
+        ```
+        
+        Include both the error and the full stack trace if reporting an exception!
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        What is the problem, question, or error?
+
+        Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
+      placeholder: |
+        * I'm trying to use the `langchain` library to do X.
+        * I expect to see Y.
+        * Instead, it does Z.
+    validations:
+      required: true
   - type: textarea
     id: system-info
     attributes:
       label: System Info
       description: Please share your system info with us.
-      placeholder: LangChain version, platform, python version, ...
+      placeholder: |
+        "pip freeze | grep langchain"
+        platform
+        python version
     validations:
       required: true
-
-  - type: textarea
-    id: who-can-help
-    attributes:
-      label: Who can help?
-      description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @
-        If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-
-        The core maintainers strive to read all issues, but tagging them will help them prioritize.
-
-        Please tag fewer than 3 people.
-
-        @hwchase17 - project lead
-
-        Tracing / Callbacks
-        - @agola11
-
-        Async
-        - @agola11
-
-        DataLoader Abstractions
-        - @eyurtsev
-
-        LLM/Chat Wrappers
-        - @hwchase17
-        - @agola11
-
-        Tools / Toolkits
-        - ...
-
-      placeholder: "@Username ..."
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: "The problem arises when using:"
-      options:
-        - label: "The official example notebooks/scripts"
-        - label: "My own modified scripts"
-
   - type: checkboxes
     id: related-components
     attributes:
@@ -77,30 +101,3 @@ body:
         - label: "Chains"
         - label: "Callbacks/Tracing"
         - label: "Async"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/ISSUE_TEMPLATE/other.yml

@@ -1,18 +0,0 @@
-name: Other Issue
-description: Raise an issue that wouldn't be covered by the other templates.
-title: "Issue: <Please write a comprehensive title after the 'Issue: ' prefix>"
-labels: [04 - Other]
-
-body:
-  - type: textarea
-    attributes:
-      label: "Issue you'd like to raise."
-      description: >
-        Please describe the issue you'd like to raise as clearly as possible.
-        Make sure to include any relevant links or references.
-
-  - type: textarea
-    attributes:
-      label: "Suggestion:"
-      description: >
-        Please outline a suggestion to improve the issue here.

.github/actions/poetry_setup/action.yml

@@ -28,6 +28,7 @@ runs:
   steps:
     - uses: actions/setup-python@v5
       name: Setup python ${{ inputs.python-version }}
+      id: setup-python
       with:
         python-version: ${{ inputs.python-version }}
 
@@ -74,7 +75,8 @@ runs:
       env:
         POETRY_VERSION: ${{ inputs.poetry-version }}
         PYTHON_VERSION: ${{ inputs.python-version }}
-      run: pipx install "poetry==$POETRY_VERSION" --python "python$PYTHON_VERSION" --verbose
+      # Install poetry using the python version installed by setup-python step.
+      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
 
     - name: Restore pip and poetry cached dependencies
       uses: actions/cache@v3

.github/workflows/_release.yml

@@ -117,11 +117,18 @@ jobs:
         #   are not found on test PyPI can be resolved and installed anyway.
         #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
         #   package because VERSION will not have been uploaded to regular PyPI yet.
-        #
+        # - attempt install again after 5 seconds if it fails because there is
+        #   sometimes a delay in availability on test pypi
         run: |
           poetry run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
-            "$PKG_NAME==$VERSION"
+            "$PKG_NAME==$VERSION" || \
+          ( \
+            sleep 5 && \
+            poetry run pip install \
+              --extra-index-url https://test.pypi.org/simple/ \
+              "$PKG_NAME==$VERSION" \
+          )
 
           # Replace all dashes in the package name with underscores,
           # since that's how Python imports packages with dashes in the name.

README.md

@@ -49,7 +49,7 @@ The LangChain libraries themselves are made up of several different packages.
 - **[`langchain-community`](libs/community)**: Third party integrations.
 - **[`langchain`](libs/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 
-![LangChain Stack](docs/static/img/langchain_stack.png)
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/img/langchain_stack.png "LangChain Architecture Overview")
 
 ## 🧱 What can you build with LangChain?
 **❓ Retrieval augmented generation**

cookbook/together_ai.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fc0309d-4d49-4bb5-bec0-bd92c6fddb28",
+   "metadata": {},
+   "source": [
+    "## Together AI + RAG\n",
+    " \n",
+    "[Together AI](https://python.langchain.com/docs/integrations/llms/together) has a broad set of OSS LLMs via inference API.\n",
+    "\n",
+    "See [here](https://api.together.xyz/playground). We use `\"mistralai/Mixtral-8x7B-Instruct-v0.1` for RAG on the Mixtral paper.\n",
+    "\n",
+    "Download the paper:\n",
+    "https://arxiv.org/pdf/2401.04088.pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --quiet pypdf chromadb tiktoken openai langchain-together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ab49327-0532-4480-804c-d066c302a322",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load\n",
+    "from langchain_community.document_loaders import PyPDFLoader\n",
+    "\n",
+    "loader = PyPDFLoader(\"~/Desktop/mixtral.pdf\")\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.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "\n",
+    "\"\"\"\n",
+    "from langchain_together.embeddings import TogetherEmbeddings\n",
+    "embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")\n",
+    "\"\"\"\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=all_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=OpenAIEmbeddings(),\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_community.llms 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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/contributing/index.mdx

@@ -40,3 +40,8 @@ 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.
+
+# 🌟 Recognition
+
+If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
+If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.

docs/docs/expression_language/cookbook/code_writing.ipynb

@@ -10,6 +10,16 @@
     "Example of how to use LCEL to write Python code."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0653c7c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-experimental langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -17,10 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import (\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import (\n",
     "    ChatPromptTemplate,\n",
     ")\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_experimental.utilities import PythonREPL\n",
     "from langchain_openai import ChatOpenAI"
    ]

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -12,6 +12,16 @@
     "One especially useful technique is to use embeddings to route a query to the most relevant prompt. Here's a very simple example."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b793a0aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -19,9 +29,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.utils.math import cosine_similarity\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",

docs/docs/expression_language/cookbook/memory.ipynb

@@ -10,6 +10,16 @@
     "This shows how to add memory to an arbitrary chain. Right now, you can use the memory classes but need to hook it up manually"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18753dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/cookbook/moderation.ipynb

@@ -10,6 +10,16 @@
     "This shows how to add in moderation (or other safeguards) around your LLM application."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6acf3505",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -19,6 +19,14 @@
     "Runnables can easily be used to string together multiple Chains"
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "0f316b5c",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -35,6 +35,14 @@
     "Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "ef79a54b",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/cookbook/prompt_size.ipynb

@@ -12,6 +12,16 @@
     "With LCEL, it's easy to add custom functionality for managing the size of prompts within your chain or agent. Let's look at simple agent example that can search Wikipedia for information."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1846587d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai wikipedia"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -19,8 +29,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install langchain wikipedia\n",
-    "\n",
     "from operator import itemgetter\n",
     "\n",
     "from langchain.agents import AgentExecutor, load_tools\n",

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install langchain openai faiss-cpu tiktoken"
+    "%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken"
    ]
   },
   {

docs/docs/expression_language/cookbook/sql_db.ipynb

@@ -19,6 +19,14 @@
     "We can replicate our SQLDatabaseChain with Runnables."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "b3121aa8",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/cookbook/tools.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install duckduckgo-search"
+    "%pip install --upgrade --quiet  langchain langchain-openai duckduckgo-search"
    ]
   },
   {

docs/docs/expression_language/get_started.ipynb

@@ -30,6 +30,14 @@
     "The most basic and common use case is chaining a prompt template and a model together. To see how this works, let's create a chain that takes a topic and generates a joke:"
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "278b0027",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-community langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -486,7 +494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/binding.ipynb

@@ -12,6 +12,16 @@
     "Suppose we have a simple prompt + model sequence:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5dad8b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/how_to/configure.ipynb

@@ -34,6 +34,16 @@
     "With LLMs we can configure things like temperature"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40ed76a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 35,

docs/docs/expression_language/how_to/decorator.ipynb

@@ -16,6 +16,16 @@
     "Let's take a look at this in action!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23b2b564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -24,6 +24,16 @@
     "IMPORTANT: By default, a lot of the LLM wrappers catch errors and retry. You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retrying and not failing."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebb61b1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/how_to/functions.ipynb

@@ -24,6 +24,14 @@
     "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "9a5fe916",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/how_to/generators.ipynb

@@ -24,6 +24,15 @@
     "## Sync version"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/how_to/inspect.ipynb

@@ -15,11 +15,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8bc5d235",
+   "id": "d816e954",
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install langchain openai faiss-cpu tiktoken"
+    "%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken"
    ]
   },
   {
@@ -29,8 +29,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from operator import itemgetter\n",
-    "\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
@@ -179,7 +177,7 @@
    "source": [
     "## Get the prompts\n",
     "\n",
-    "An important part of every chain is the prompts that are used. You can get the graphs present in the chain:"
+    "An important part of every chain is the prompts that are used. You can get the prompts present in the chain:"
    ]
   },
   {

docs/docs/expression_language/how_to/map.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "e2596041-9b76-4e74-836f-e6235086bbf0",
    "metadata": {},
    "source": [
@@ -26,6 +26,16 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2627ffd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,

docs/docs/expression_language/how_to/message_history.ipynb

@@ -41,7 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U langchain redis anthropic"
+    "%pip install --upgrade --quiet  langchain redis anthropic"
    ]
   },
   {

docs/docs/expression_language/how_to/passthrough.ipynb

@@ -28,6 +28,16 @@
     "See the example below:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e169b952",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,

docs/docs/expression_language/interface.ipynb

@@ -50,6 +50,14 @@
     "Let's take a look at these methods. To do so, we'll create a super simple PromptTemplate + ChatModel chain."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "57768739",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-community langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/expression_language/why.ipynb

@@ -35,6 +35,14 @@
     "To better understand the value of LCEL, it's helpful to see it in action and think about how we might recreate similar functionality without it. In this walkthrough we'll do just that with our [basic example](/docs/expression_language/get_started#basic_example) from the get started section. We'll take our simple prompt + model chain, which under the hood already defines a lot of functionality, and see what it would take to recreate all of it."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "b99b47ec",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/docs/get_started/introduction.mdx

@@ -14,7 +14,7 @@ This framework consists of several parts.
 - **[LangServe](/docs/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
-![LangChain Diagram](/svg/langchain_stack.svg)
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](/svg/langchain_stack.svg "LangChain Framework Overview")
 
 Together, these products simplify the entire application lifecycle:
 - **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.

docs/docs/get_started/quickstart.mdx

@@ -73,7 +73,7 @@ For this getting started guide, we will provide two options: using OpenAI (a pop
 First we'll need to import the LangChain x OpenAI integration package.
 
 ```shell
-pip install langchain_openai
+pip install langchain-openai
 ```
 
 Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:
@@ -182,7 +182,14 @@ You can then use a retriever to fetch only the most relevant pieces and pass tho
 In this process, we will look up relevant documents from a *Retriever* and then pass them into the prompt.
 A Retriever can be backed by anything - a SQL table, the internet, etc - but in this instance we will populate a vector store and use that as a retriever. For more information on vectorstores, see [this documentation](/docs/modules/data_connection/vectorstores).
 
-First, we need to load the data that we want to index:
+First, we need to load the data that we want to index. In order to do this, we will use the WebBaseLoader. This requires installing [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/):
+
+```
+```shell
+pip install beautifulsoup4
+```
+
+After that, we can import and use WebBaseLoader.
 
 
 ```python

docs/docs/guides/debugging.md

@@ -12,7 +12,7 @@ Platforms with tracing capabilities like [LangSmith](/docs/langsmith/) and [Wand
 
 For anyone building production-grade LLM applications, we highly recommend using a platform like this.
 
-![LangSmith run](../../static/img/run_details.png)
+![Screenshot of the LangSmith debugging interface showing an AgentExecutor run with input and output details, and a run tree visualization.](../../static/img/run_details.png "LangSmith Debugging Interface")
 
 ## `set_debug` and `set_verbose`
 

docs/docs/guides/evaluation/comparison/custom.ipynb

@@ -104,7 +104,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install anthropic\n",
+    "%pip install --upgrade --quiet  anthropic\n",
     "# %env ANTHROPIC_API_KEY=YOUR_API_KEY"
    ]
   },

docs/docs/guides/evaluation/examples/comparisons.ipynb

@@ -23,6 +23,15 @@
     "In this example, you will use gpt-4 to select which output is preferred."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb

@@ -318,7 +318,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install ChatAnthropic\n",
+    "%pip install --upgrade --quiet  anthropic\n",
     "# %env ANTHROPIC_API_KEY=<API_KEY>"
    ]
   },
@@ -464,4 +464,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/docs/guides/evaluation/string/custom.ipynb

@@ -23,7 +23,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install evaluate > /dev/null"
+    "%pip install --upgrade --quiet  evaluate > /dev/null"
    ]
   },
   {

docs/docs/guides/evaluation/string/scoring_eval_chain.ipynb

@@ -18,6 +18,15 @@
     "Below is an example demonstrating the usage of `LabeledScoreStringEvalChain` using the default prompt:\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,

docs/docs/guides/evaluation/string/string_distance.ipynb

@@ -29,7 +29,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install rapidfuzz"
+    "%pip install --upgrade --quiet  rapidfuzz"
    ]
   },
   {

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

@@ -14,6 +14,16 @@
     "In this example, you will make a simple trajectory evaluator that uses an LLM to determine if any actions were unnecessary."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c96b340",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -140,4 +150,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb

@@ -17,6 +17,16 @@
     "For more information, check out the reference docs for the [TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain) for more info."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4d22262",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -177,7 +187,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install anthropic\n",
+    "%pip install --upgrade --quiet  anthropic\n",
     "# ANTHROPIC_API_KEY=<YOUR ANTHROPIC API KEY>"
    ]
   },
@@ -300,4 +310,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/docs/guides/fallbacks.ipynb

@@ -26,6 +26,16 @@
     "IMPORTANT: By default, a lot of the LLM wrappers catch errors and retry. You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retrying and not failing."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a449a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/guides/local_llms.ipynb

@@ -201,7 +201,7 @@
     "\n",
     "* E.g., for Llama-7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
     "* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama?tab=readme-ov-file#model-library), e.g., `ollama pull llama2:13b`\n",
-    "* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html)"
+    "* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.ollama.Ollama.html)"
    ]
   },
   {
@@ -241,7 +241,7 @@
     "\n",
     "As noted above, see the [API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
     "\n",
-    "From the [llama.cpp docs](https://python.langchain.com/docs/integrations/llms/llamacpp), a few are worth commenting on:\n",
+    "From the [llama.cpp API reference docs](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.llamacpp.LlamaCpp.htm), a few are worth commenting on:\n",
     "\n",
     "`n_gpu_layers`: number of layers to be loaded into GPU memory\n",
     "\n",
@@ -277,7 +277,7 @@
    "source": [
     "%env CMAKE_ARGS=\"-DLLAMA_METAL=on\"\n",
     "%env FORCE_CMAKE=1\n",
-    "%pip install -U llama-cpp-python --no-cache-dirclear"
+    "%pip install --upgrade --quiet  llama-cpp-python --no-cache-dirclear"
    ]
   },
   {
@@ -378,9 +378,9 @@
    "source": [
     "### GPT4All\n",
     "\n",
-    "We can use model weights downloaded from [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all) model explorer.\n",
+    "We can use model weights downloaded from [GPT4All](/docs/integrations/llms/gpt4all) model explorer.\n",
     "\n",
-    "Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html?highlight=gpt4all#langchain.llms.gpt4all.GPT4All) to set parameters of interest."
+    "Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.gpt4all.GPT4All.html) to set parameters of interest."
    ]
   },
   {
@@ -390,7 +390,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install gpt4all\n"
+    "%pip install gpt4all"
    ]
   },
   {
@@ -582,9 +582,9 @@
    "source": [
     "## Use cases\n",
     "\n",
-    "Given an `llm` created from one of the models above, you can use it for [many use cases](docs/use_cases).\n",
+    "Given an `llm` created from one of the models above, you can use it for [many use cases](/docs/use_cases/).\n",
     "\n",
-    "For example, here is a guide to [RAG](docs/use_cases/question_answering/local_retrieval_qa) with local LLMs.\n",
+    "For example, here is a guide to [RAG](/docs/use_cases/question_answering/local_retrieval_qa) with local LLMs.\n",
     "\n",
     "In general, use cases for local LLMs can be driven by at least two factors:\n",
     "\n",
@@ -611,7 +611,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/guides/model_laboratory.ipynb

@@ -12,6 +12,16 @@
     "LangChain provides the concept of a ModelLaboratory to test out and try different models."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12ebae56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb

@@ -28,15 +28,23 @@
     "Below you will find the use case on how to leverage anonymization in LangChain."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai langchain-experimental presidio-analyzer presidio-anonymizer spacy Faker"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install necessary packages\n",
-    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
-    "# ! python -m spacy download en_core_web_lg"
+    "# Download model\n",
+    "!python -m spacy download en_core_web_lg"
    ]
   },
   {

docs/docs/guides/privacy/presidio_data_anonymization/multi_language.ipynb

@@ -41,15 +41,21 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai langchain-experimental presidio-analyzer presidio-anonymizer spacy Faker"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install necessary packages\n",
-    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
-    "# ! python -m spacy download en_core_web_lg"
+    "# Download model\n",
+    "!python -m spacy download en_core_web_lg"
    ]
   },
   {
@@ -239,7 +245,7 @@
    "outputs": [],
    "source": [
     "# Install necessary packages\n",
-    "# ! pip install fasttext langdetect"
+    "%pip install --upgrade --quiet  fasttext langdetect"
    ]
   },
   {

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

@@ -31,15 +31,21 @@
     "### Iterative process of upgrading the anonymizer"
    ]
   },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-experimental langchain-openai presidio-analyzer presidio-anonymizer spacy Faker faiss-cpu tiktoken"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install necessary packages\n",
-    "# !pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker faiss-cpu tiktoken\n",
-    "# ! python -m spacy download en_core_web_lg"
+    "# Download model\n",
+    "! python -m spacy download en_core_web_lg"
    ]
   },
   {

docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb

@@ -56,7 +56,7 @@
    "outputs": [],
    "source": [
     "# Install necessary packages\n",
-    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
+    "%pip install --upgrade --quiet  langchain langchain-experimental langchain-openai presidio-analyzer presidio-anonymizer spacy Faker\n",
     "# ! python -m spacy download en_core_web_lg"
    ]
   },

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -24,7 +24,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install boto3 nltk"
+    "%pip install --upgrade --quiet  boto3 nltk"
    ]
   },
   {
@@ -37,7 +37,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -U langchain_experimental"
+    "%pip install --upgrade --quiet  langchain_experimental"
    ]
   },
   {
@@ -50,7 +50,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -U langchain pydantic"
+    "%pip install --upgrade --quiet  langchain pydantic"
    ]
   },
   {
@@ -527,7 +527,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install huggingface_hub"
+    "%pip install --upgrade --quiet  huggingface_hub"
    ]
   },
   {

docs/docs/guides/safety/hugging_face_prompt_injection.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"optimum[onnxruntime]\""
+    "%pip install --upgrade --quiet  \"optimum[onnxruntime]\" langchain transformers langchain-experimental langchain-openai"
    ]
   },
   {

docs/docs/integrations/callbacks/argilla.ipynb

@@ -43,8 +43,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install argilla --upgrade\n",
-    "!pip install openai"
+    "%pip install --upgrade --quiet  langchain langchain-openai argilla"
    ]
   },
   {

docs/docs/integrations/callbacks/confident.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install deepeval --upgrade"
+    "%pip install --upgrade --quiet  langchain langchain-openai deepeval"
    ]
   },
   {

docs/docs/integrations/callbacks/context.ipynb

@@ -36,7 +36,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install context-python --upgrade"
+    "%pip install --upgrade --quiet  langchain langchain-openai context-python"
    ]
   },
   {

docs/docs/integrations/callbacks/infino.ipynb

@@ -34,9 +34,9 @@
    "outputs": [],
    "source": [
     "# Install necessary dependencies.\n",
-    "!pip install -q infinopy\n",
-    "!pip install -q matplotlib\n",
-    "!pip install -q tiktoken"
+    "%pip install --upgrade --quiet  infinopy\n",
+    "%pip install --upgrade --quiet  matplotlib\n",
+    "%pip install --upgrade --quiet  tiktoken"
    ]
   },
   {

docs/docs/integrations/callbacks/labelstudio.ipynb

@@ -56,7 +56,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -U label-studio label-studio-sdk openai"
+    "%pip install --upgrade --quiet langchain label-studio label-studio-sdk langchain-openai"
    ]
   },
   {

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -32,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install promptlayer --upgrade"
+    "%pip install --upgrade --quiet  promptlayer --upgrade"
    ]
   },
   {

docs/docs/integrations/callbacks/sagemaker_tracking.ipynb

@@ -38,9 +38,9 @@
    },
    "outputs": [],
    "source": [
-    "!pip install sagemaker\n",
-    "!pip install openai\n",
-    "!pip install google-search-results"
+    "%pip install --upgrade --quiet  sagemaker\n",
+    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  google-search-results"
    ]
   },
   {

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install trubrics"
+    "%pip install --upgrade --quiet  trubrics"
    ]
   },
   {

docs/docs/integrations/chat/anthropic.ipynb

@@ -129,7 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install langchain-anthropic"
+    "%pip install --upgrade --quiet  langchain-anthropic"
    ]
   },
   {

docs/docs/integrations/chat/anyscale.ipynb

@@ -33,7 +33,7 @@
    },
    "outputs": [],
    "source": [
-    "# !pip install openai"
+    "%pip install --upgrade --quiet  langchain-openai"
    ]
   },
   {

docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb

@@ -54,18 +54,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO] [09-15 20:00:29] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant\n"
-     ]
-    }
-   ],
+   "source": [
+    "## Set up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\"\"\"For basic init and call\"\"\"\n",
     "import os\n",
@@ -73,85 +72,105 @@
     "from langchain_community.chat_models import QianfanChatEndpoint\n",
     "from langchain_core.language_models.chat_models import HumanMessage\n",
     "\n",
-    "os.environ[\"QIANFAN_AK\"] = \"your_ak\"\n",
-    "os.environ[\"QIANFAN_SK\"] = \"your_sk\"\n",
-    "\n",
-    "chat = QianfanChatEndpoint(\n",
-    "    streaming=True,\n",
-    ")\n",
-    "res = chat([HumanMessage(content=\"write a funny joke\")])"
+    "os.environ[\"QIANFAN_AK\"] = \"Your_api_key\"\n",
+    "os.environ[\"QIANFAN_SK\"] = \"You_secret_Key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO] [09-15 20:00:36] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant\n",
-      "[INFO] [09-15 20:00:37] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "AIMessage(content='您好！请问您需要什么帮助？我将尽力回答您的问题。')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = QianfanChatEndpoint(streaming=True)\n",
+    "messages = [HumanMessage(content=\"Hello\")]\n",
+    "chat.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='您好！有什么我可以帮助您的吗？')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.ainvoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content='您好！有什么我可以帮助您的吗？')]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.batch([messages])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chat resp: content='您好，您似乎输入' additional_kwargs={} example=False\n",
-      "chat resp: content='了一个话题标签，请问需要我帮您找到什么资料或者帮助您解答什么问题吗？' additional_kwargs={} example=False\n",
-      "chat resp: content='' additional_kwargs={} example=False\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO] [09-15 20:00:39] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "generations=[[ChatGeneration(text=\"The sea is a vast expanse of water that covers much of the Earth's surface. It is a source of travel, trade, and entertainment, and is also a place of scientific exploration and marine conservation. The sea is an important part of our world, and we should cherish and protect it.\", generation_info={'finish_reason': 'finished'}, message=AIMessage(content=\"The sea is a vast expanse of water that covers much of the Earth's surface. It is a source of travel, trade, and entertainment, and is also a place of scientific exploration and marine conservation. The sea is an important part of our world, and we should cherish and protect it.\", additional_kwargs={}, example=False))]] llm_output={} run=[RunInfo(run_id=UUID('d48160a6-5960-4c1d-8a0e-90e6b51a209b'))]\n",
-      "astream content='The sea is a vast' additional_kwargs={} example=False\n",
-      "astream content=' expanse of water, a place of mystery and adventure. It is the source of many cultures and civilizations, and a center of trade and exploration. The sea is also a source of life and beauty, with its unique marine life and diverse' additional_kwargs={} example=False\n",
-      "astream content=' coral reefs. Whether you are swimming, diving, or just watching the sea, it is a place that captivates the imagination and transforms the spirit.' additional_kwargs={} example=False\n"
+      "您好！有什么我可以帮助您的吗？\n"
      ]
     }
    ],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import QianfanChatEndpoint\n",
-    "\n",
-    "chatLLM = QianfanChatEndpoint(\n",
-    "    streaming=True,\n",
-    ")\n",
-    "res = chatLLM.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
-    "for r in res:\n",
-    "    print(\"chat resp:\", r)\n",
-    "\n",
-    "\n",
-    "async def run_aio_generate():\n",
-    "    resp = await chatLLM.agenerate(\n",
-    "        messages=[[HumanMessage(content=\"write a 20 words sentence about sea.\")]]\n",
-    "    )\n",
-    "    print(resp)\n",
-    "\n",
-    "\n",
-    "await run_aio_generate()\n",
-    "\n",
-    "\n",
-    "async def run_aio_stream():\n",
-    "    async for res in chatLLM.astream(\n",
-    "        [HumanMessage(content=\"write a 20 words sentence about sea.\")]\n",
-    "    ):\n",
-    "        print(\"astream\", res)\n",
-    "\n",
-    "\n",
-    "await run_aio_stream()"
+    "try:\n",
+    "    for chunk in chat.stream(messages):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "except TypeError as e:\n",
+    "    print(\"\")"
    ]
   },
   {
@@ -161,39 +180,36 @@
    "source": [
     "## Use different models in Qianfan\n",
     "\n",
-    "In the case you want to deploy your own model based on Ernie Bot or third-party open-source model, you could follow these steps:\n",
+    "The default model is ERNIE-Bot-turbo, in the case you want to deploy your own model based on Ernie Bot or third-party open-source model, you could follow these steps:\n",
     "\n",
-    "- 1. （Optional, if the model are included in the default models, skip it）Deploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
-    "- 2. Set up the field called `endpoint` in the initialization:"
+    "1. (Optional, if the model are included in the default models, skip it) Deploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
+    "2. Set up the field called `endpoint` in the initialization:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO] [09-15 20:00:50] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/bloomz_7b1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "content='你好！很高兴见到你。' additional_kwargs={} example=False\n"
-     ]
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Hello，可以回答问题了，我会竭尽全力为您解答，请问有什么问题吗？')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "chatBloom = QianfanChatEndpoint(\n",
+    "chatBot = QianfanChatEndpoint(\n",
     "    streaming=True,\n",
-    "    model=\"BLOOMZ-7B\",\n",
+    "    model=\"ERNIE-Bot\",\n",
     ")\n",
-    "res = chatBloom([HumanMessage(content=\"hi\")])\n",
-    "print(res)"
+    "\n",
+    "messages = [HumanMessage(content=\"Hello\")]\n",
+    "chatBot.invoke(messages)"
    ]
   },
   {
@@ -212,35 +228,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[INFO] [09-15 20:00:57] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "content='您好，您似乎输入' additional_kwargs={} example=False\n",
-      "content='了一个文本字符串，但并没有给出具体的问题或场景。' additional_kwargs={} example=False\n",
-      "content='如果您能提供更多信息，我可以更好地回答您的问题。' additional_kwargs={} example=False\n",
-      "content='' additional_kwargs={} example=False\n"
-     ]
+     "data": {
+      "text/plain": [
+       "AIMessage(content='您好！有什么我可以帮助您的吗？')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "res = chat.stream(\n",
-    "    [HumanMessage(content=\"hi\")],\n",
+    "chat.invoke(\n",
+    "    [HumanMessage(content=\"Hello\")],\n",
     "    **{\"top_p\": 0.4, \"temperature\": 0.1, \"penalty_score\": 1},\n",
-    ")\n",
-    "\n",
-    "for r in res:\n",
-    "    print(r)"
+    ")"
    ]
   }
  ],
@@ -260,11 +266,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.18"
   },
   "vscode": {
    "interpreter": {
-    "hash": "6fa70026b407ae751a5c9e6bd7f7d482379da8ad616f98512780b705c84ee157"
+    "hash": "58f7cb64c3a06383b7f18d2a11305edccbad427293a2b4afa7abe8bfc810d4bb"
    }
   }
  },

docs/docs/integrations/chat/bedrock.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install boto3"
+    "%pip install --upgrade --quiet  boto3"
    ]
   },
   {

docs/docs/integrations/chat/ernie.ipynb

@@ -16,29 +16,58 @@
     "# ErnieBotChat\n",
     "\n",
     "[ERNIE-Bot](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11) is a large language model developed by Baidu, covering a huge amount of Chinese data.\n",
-    "This notebook covers how to get started with ErnieBot chat models.\n",
+    "This notebook covers how to get started with ErnieBot chat models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Deprecated Warning**\n",
+    "\n",
+    "We recommend users using `langchain_community.chat_models.ErnieBotChat` \n",
+    "to use `langchain_community.chat_models.QianfanChatEndpoint` instead.\n",
+    "\n",
+    "documentation for `QianfanChatEndpoint` is [here](./baidu_qianfan_endpoint).\n",
+    "\n",
+    "they are 4 why we recommend users to use `QianfanChatEndpoint`:\n",
     "\n",
-    "**Note:** We recommend users using this class to switch to [Baidu Qianfan](./baidu_qianfan_endpoint). they are 3 why we recommend users to use `QianfanChatEndpoint`:\n",
     "1. `QianfanChatEndpoint` support more LLM in the Qianfan platform.\n",
     "2. `QianfanChatEndpoint` support streaming mode.\n",
     "3. `QianfanChatEndpoint` support function calling usgage.\n",
-    "\n",
+    "4. `ErnieBotChat` is lack of maintenance and deprecated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Some tips for migration:\n",
+    "\n",
     "- change `ernie_client_id` to `qianfan_ak`, also change `ernie_client_secret` to `qianfan_sk`.\n",
-    "- install `qianfan` package. \n",
-    "    ```\n",
-    "    pip install qianfan\n",
-    "    ```"
+    "- install `qianfan` package. like `pip install qianfan`\n",
+    "- change `ErnieBotChat` to `QianfanChatEndpoint`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import HumanMessage\n",
-    "from langchain_community.chat_models import ErnieBotChat"
+    "from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\n",
+    "\n",
+    "chat = QianfanChatEndpoint(\n",
+    "    qianfan_ak=\"your qianfan ak\",\n",
+    "    qianfan_sk=\"your qianfan sk\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage"
    ]
   },
   {
@@ -47,6 +76,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_community.chat_models import ErnieBotChat\n",
+    "\n",
     "chat = ErnieBotChat(\n",
     "    ernie_client_id=\"YOUR_CLIENT_ID\", ernie_client_secret=\"YOUR_CLIENT_SECRET\"\n",
     ")"

docs/docs/integrations/chat/everlyai.ipynb

@@ -34,7 +34,7 @@
    },
    "outputs": [],
    "source": [
-    "# !pip install openai"
+    "%pip install --upgrade --quiet  langchain-openai"
    ]
   },
   {

docs/docs/integrations/chat/gigachat.ipynb

@@ -19,7 +19,7 @@
    },
    "outputs": [],
    "source": [
-    "# !pip install gigachat"
+    "%pip install --upgrade --quiet  gigachat"
    ]
   },
   {

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U --quiet langchain-google-genai pillow"
+    "%pip install --upgrade --quiet  langchain-google-genai pillow"
    ]
   },
   {

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) customer data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
     "\n",
-    "To use `Google Cloud Vertex AI` PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
+    "To use `Google Cloud Vertex AI` PaLM you must have the `langchain-google-vertexai` Python package installed and either:\n",
     "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
     "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
     "\n",
@@ -35,13 +35,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "!pip install -U langchain-google-vertexai"
+    "%pip install --upgrade --quiet  langchain-google-vertexai"
    ]
   },
   {
@@ -50,8 +61,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_google_vertexai import ChatVertexAI\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_google_vertexai import ChatVertexAI"
    ]
   },
   {

docs/docs/integrations/chat/gpt_router.ipynb

@@ -58,7 +58,7 @@
     }
    ],
    "source": [
-    "%pip install GPTRouter"
+    "%pip install --upgrade --quiet  GPTRouter"
    ]
   },
   {

docs/docs/integrations/chat/huggingface.ipynb

@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "%pip install -q text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2"
+    "%pip install --upgrade --quiet  text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2"
    ]
   },
   {

docs/docs/integrations/chat/llama_api.ipynb

@@ -25,7 +25,7 @@
    "id": "f5b652cf",
    "metadata": {},
    "source": [
-    "!pip install -U llamaapi"
+    "%pip install --upgrade --quiet  llamaapi"
    ]
   },
   {

docs/docs/integrations/chat/llama_edge.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LlamaEdge\n",
+    "\n",
+    "[LlamaEdge](https://github.com/second-state/LlamaEdge) allows you to chat with LLMs of [GGUF](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/README.md) format both locally and via chat service.\n",
+    "\n",
+    "- `LlamaEdgeChatService` provides developers an OpenAI API compatible service to chat with LLMs via HTTP requests.\n",
+    "\n",
+    "- `LlamaEdgeChatLocal` enables developers to chat with LLMs locally (coming soon).\n",
+    "\n",
+    "Both `LlamaEdgeChatService` and `LlamaEdgeChatLocal` run on the infrastructure driven by [WasmEdge Runtime](https://wasmedge.org/), which provides a lightweight and portable WebAssembly container environment for LLM inference tasks.\n",
+    "\n",
+    "## Chat via API Service\n",
+    "\n",
+    "`LlamaEdgeChatService` works on the `llama-api-server`. Following the steps in [llama-api-server quick-start](https://github.com/second-state/llama-utils/tree/main/api-server#readme), you can host your own API service so that you can chat with any models you like on any device you have anywhere as long as the internet is available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.llama_edge import LlamaEdgeChatService\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chat with LLMs in the non-streaming mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Bot] Hello! The capital of France is Paris.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# service url\n",
+    "service_url = \"https://b008-54-186-154-209.ngrok-free.app\"\n",
+    "\n",
+    "# create wasm-chat service instance\n",
+    "chat = LlamaEdgeChatService(service_url=service_url)\n",
+    "\n",
+    "# create message sequence\n",
+    "system_message = SystemMessage(content=\"You are an AI assistant\")\n",
+    "user_message = HumanMessage(content=\"What is the capital of France?\")\n",
+    "messages = [system_message, user_message]\n",
+    "\n",
+    "# chat with wasm-chat service\n",
+    "response = chat(messages)\n",
+    "\n",
+    "print(f\"[Bot] {response.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chat with LLMs in the streaming mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Bot]   Hello! I'm happy to help you with your question. The capital of Norway is Oslo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# service url\n",
+    "service_url = \"https://b008-54-186-154-209.ngrok-free.app\"\n",
+    "\n",
+    "# create wasm-chat service instance\n",
+    "chat = LlamaEdgeChatService(service_url=service_url, streaming=True)\n",
+    "\n",
+    "# create message sequence\n",
+    "system_message = SystemMessage(content=\"You are an AI assistant\")\n",
+    "user_message = HumanMessage(content=\"What is the capital of Norway?\")\n",
+    "messages = [\n",
+    "    system_message,\n",
+    "    user_message,\n",
+    "]\n",
+    "\n",
+    "output = \"\"\n",
+    "for chunk in chat.stream(messages):\n",
+    "    # print(chunk.content, end=\"\", flush=True)\n",
+    "    output += chunk.content\n",
+    "\n",
+    "print(f\"[Bot] {output}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -44,7 +44,7 @@
     }
    ],
    "source": [
-    "%pip install -U --quiet langchain-nvidia-ai-endpoints"
+    "%pip install --upgrade --quiet  langchain-nvidia-ai-endpoints"
    ]
   },
   {
@@ -795,7 +795,7 @@
     }
    ],
    "source": [
-    "%pip install -U --quiet langchain"
+    "%pip install --upgrade --quiet  langchain"
    ]
   },
   {

docs/docs/integrations/chat/ollama.ipynb

@@ -264,7 +264,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install pillow"
+    "%pip install --upgrade --quiet  pillow"
    ]
   },
   {

docs/docs/integrations/chat/tongyi.ipynb

@@ -36,7 +36,7 @@
    "outputs": [],
    "source": [
     "# Install the package\n",
-    "!pip install dashscope"
+    "%pip install --upgrade --quiet  dashscope"
    ]
   },
   {

docs/docs/integrations/chat/volcengine_maas.ipynb

@@ -32,7 +32,7 @@
    "outputs": [],
    "source": [
     "# Install the package\n",
-    "!pip install volcengine"
+    "%pip install --upgrade --quiet  volcengine"
    ]
   },
   {

docs/docs/integrations/chat/wasm_chat.ipynb

@@ -1,85 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Wasm Chat\n",
-    "\n",
-    "`Wasm-chat` allows you to chat with LLMs of [GGUF](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/README.md) format both locally and via chat service.\n",
-    "\n",
-    "- `WasmChatService` provides developers an OpenAI API compatible service to chat with LLMs via HTTP requests.\n",
-    "\n",
-    "- `WasmChatLocal` enables developers to chat with LLMs locally (coming soon).\n",
-    "\n",
-    "Both `WasmChatService` and `WasmChatLocal` run on the infrastructure driven by [WasmEdge Runtime](https://wasmedge.org/), which provides a lightweight and portable WebAssembly container environment for LLM inference tasks.\n",
-    "\n",
-    "## Chat via API Service\n",
-    "\n",
-    "`WasmChatService` provides chat services by the `llama-api-server`. Following the steps in [llama-api-server quick-start](https://github.com/second-state/llama-utils/tree/main/api-server#readme), you can host your own API service so that you can chat with any models you like on any device you have anywhere as long as the internet is available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.wasm_chat import WasmChatService\n",
-    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Bot] Paris\n"
-     ]
-    }
-   ],
-   "source": [
-    "# service url\n",
-    "service_url = \"https://b008-54-186-154-209.ngrok-free.app\"\n",
-    "\n",
-    "# create wasm-chat service instance\n",
-    "chat = WasmChatService(service_url=service_url)\n",
-    "\n",
-    "# create message sequence\n",
-    "system_message = SystemMessage(content=\"You are an AI assistant\")\n",
-    "user_message = HumanMessage(content=\"What is the capital of France?\")\n",
-    "messages = [system_message, user_message]\n",
-    "\n",
-    "# chat with wasm-chat service\n",
-    "response = chat(messages)\n",
-    "\n",
-    "print(f\"[Bot] {response.content}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/yandex.ipynb

@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install yandexcloud"
+    "%pip install --upgrade --quiet  yandexcloud"
    ]
   },
   {

docs/docs/integrations/chat/zhipuai.ipynb

@@ -32,7 +32,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-       "# !pip install zhipuai"
+       "%pip install --upgrade --quiet  zhipuai"
       ]
      },
      {

docs/docs/integrations/chat_loaders/facebook.ipynb

@@ -369,7 +369,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %pip install -U openai --quiet"
+    "%pip install --upgrade --quiet  langchain-openai"
    ]
   },
   {

docs/docs/integrations/chat_loaders/gmail.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client"
+    "%pip install --upgrade --quiet  google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client"
    ]
   },
   {

docs/docs/integrations/chat_loaders/imessage.ipynb

@@ -213,7 +213,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %pip install -U openai --quiet"
+    "%pip install --upgrade --quiet  langchain-openai"
    ]
   },
   {

docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U langchain openai"
+    "%pip install --upgrade --quiet  langchain langchain-openai"
    ]
   },
   {

docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U langchain openai"
+    "%pip install --upgrade --quiet  langchain langchain-openai"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_cdk.ipynb

@@ -41,7 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-cdk"
+    "%pip install --upgrade --quiet  airbyte-cdk"
    ]
   },
   {
@@ -61,7 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install \"source_github@git+https://github.com/airbytehq/airbyte.git@master#subdirectory=airbyte-integrations/connectors/source-github\""
+    "%pip install --upgrade --quiet  \"source_github@git+https://github.com/airbytehq/airbyte.git@master#subdirectory=airbyte-integrations/connectors/source-github\""
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_gong.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-gong"
+    "%pip install --upgrade --quiet  airbyte-source-gong"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_hubspot.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-hubspot"
+    "%pip install --upgrade --quiet  airbyte-source-hubspot"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_salesforce.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-salesforce"
+    "%pip install --upgrade --quiet  airbyte-source-salesforce"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_shopify.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-shopify"
+    "%pip install --upgrade --quiet  airbyte-source-shopify"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_stripe.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-stripe"
+    "%pip install --upgrade --quiet  airbyte-source-stripe"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_typeform.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-typeform"
+    "%pip install --upgrade --quiet  airbyte-source-typeform"
    ]
   },
   {

docs/docs/integrations/document_loaders/airbyte_zendesk_support.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install airbyte-source-zendesk-support"
+    "%pip install --upgrade --quiet  airbyte-source-zendesk-support"
    ]
   },
   {

docs/docs/integrations/document_loaders/airtable.ipynb

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install pyairtable"
+    "%pip install --upgrade --quiet  pyairtable"
    ]
   },
   {

docs/docs/integrations/document_loaders/alibaba_cloud_maxcompute.ipynb

@@ -37,7 +37,7 @@
     }
    ],
    "source": [
-    "!pip install pyodps"
+    "%pip install --upgrade --quiet  pyodps"
    ]
   },
   {

docs/docs/integrations/document_loaders/amazon_textract.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install boto3 openai tiktoken python-dotenv"
+    "%pip install --upgrade --quiet  boto3 langchain-openai tiktoken python-dotenv"
    ]
   },
   {
@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install \"amazon-textract-caller>=0.2.0\""
+    "%pip install --upgrade --quiet  \"amazon-textract-caller>=0.2.0\""
    ]
   },
   {

docs/docs/integrations/document_loaders/apify_dataset.ipynb

@@ -24,7 +24,7 @@
    },
    "outputs": [],
    "source": [
-    "#!pip install apify-client"
+    "%pip install --upgrade --quiet  apify-client"
    ]
   },
   {

docs/docs/integrations/document_loaders/arxiv.ipynb

@@ -37,7 +37,7 @@
    },
    "outputs": [],
    "source": [
-    "#!pip install arxiv"
+    "%pip install --upgrade --quiet  arxiv"
    ]
   },
   {
@@ -59,7 +59,7 @@
    },
    "outputs": [],
    "source": [
-    "#!pip install pymupdf"
+    "%pip install --upgrade --quiet  pymupdf"
    ]
   },
   {

docs/docs/integrations/document_loaders/assemblyai.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install assemblyai"
+    "%pip install --upgrade --quiet  assemblyai"
    ]
   },
   {

docs/docs/integrations/document_loaders/async_chromium.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -q playwright beautifulsoup4\n",
+    "%pip install --upgrade --quiet  playwright beautifulsoup4\n",
     "! playwright install"
    ]
   },

docs/docs/integrations/document_loaders/aws_s3_directory.ipynb

@@ -23,7 +23,7 @@
    },
    "outputs": [],
    "source": [
-    "#!pip install boto3"
+    "%pip install --upgrade --quiet  boto3"
    ]
   },
   {

docs/docs/integrations/document_loaders/aws_s3_file.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install boto3"
+    "%pip install --upgrade --quiet  boto3"
    ]
   },
   {