core[patch]: support handle_tool_error=(Exception, ...) tuple

Fixes #27411 

**Description:** Adds `template_format` to the `ImagePromptTemplate`
class and updates passing in the `template_format` parameter from
ChatPromptTemplate instead of the hardcoded "f-string".
Also updated docs and typing related to `template_format` to be more
up-to-date and specific.

**Dependencies:** None

**Add tests and docs**: Added unit tests to validate fix. Needed to
update `test_chat` snapshot due to adding new attribute
`template_format` in `ImagePromptTemplate`.

---------

Co-authored-by: Vadym Barda <vadym@langchain.dev>

.github/actions/poetry_setup/action.yml

@@ -55,14 +55,11 @@ runs:
         # Refresh the shell hashtable, to ensure correct `which` output.
         hash -r
 
-        # Extract the major.minor version of python
-        PYTHON_MAJOR_MINOR=$(echo "$PYTHON_VERSION" | sed -E 's/^([0-9]+\.[0-9]+).*/\1/')
-
         # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
         # Delete and recreate the softlinks pipx expects to have.
         rm /opt/pipx/venvs/poetry/bin/python
         cd /opt/pipx/venvs/poetry/bin
-        ln -s "$(which "python$PYTHON_MAJOR_MINOR")" python
+        ln -s "$(which "python$PYTHON_VERSION")" python
         chmod +x python
         cd /opt/pipx_bin/
         ln -s /opt/pipx/venvs/poetry/bin/poetry poetry

.github/scripts/check_diff.py

@@ -110,7 +110,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         return _get_pydantic_test_configs(dir_)
 
     if dir_ == "libs/core":
-        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13.0-rc.2"]
+        py_versions = ["3.9", "3.10", "3.11", "3.12"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
         # milvus poetry doesn't allow 3.12 because they
@@ -120,13 +120,13 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
         # community extended test resolution in 3.12 is slow
         # even in uv
-        py_versions = ["3.9", "3.11", "3.13.0-rc.2"]
+        py_versions = ["3.9", "3.11"]
 
     elif dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
-        py_versions = ["3.9", "3.11", "3.13.0-rc.2"]
+        py_versions = ["3.9", "3.11"]
     else:
-        py_versions = ["3.9", "3.12", "3.13.0-rc.2"]
+        py_versions = ["3.9", "3.12"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 

.github/workflows/_release.yml

@@ -294,7 +294,6 @@ jobs:
           VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          UNSTRUCTURED_API_KEY: ${{ secrets.UNSTRUCTURED_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/api_doc_build.yml

@@ -10,6 +10,7 @@ env:
 
 jobs:
   build:
+    if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     permissions: write-all
     steps:
@@ -65,6 +66,31 @@ jobs:
         with:
           repository: langchain-ai/langchain-experimental
           path: langchain-experimental
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-milvus
+          path: langchain-milvus
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-unstructured
+          path: langchain-unstructured
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-databricks
+          path: langchain-databricks
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-ibm
+          path: langchain-ibm
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-azure
+          path: langchain-azure
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-mongodb
+          path: langchain-mongodb
+
 
 
       - name: Set Git config
@@ -73,7 +99,7 @@ jobs:
           git config --local user.email "actions@github.com"
           git config --local user.name "Github Actions"
           
-      - name: Move google libs
+      - name: Move libs
         run: |
           rm -rf \
             langchain/libs/partners/google-genai \
@@ -87,12 +113,18 @@ jobs:
             langchain/libs/partners/ai21 \
             langchain/libs/partners/together \
             langchain/libs/standard-tests \
-            langchain/libs/experimental
+            langchain/libs/experimental \
+            langchain/libs/partners/milvus \
+            langchain/libs/partners/unstructured \
+            langchain/libs/partners/databricks \
+            langchain/libs/partners/ibm \
+            langchain/libs/partners/azure-dynamic-sessions \
+            langchain/libs/partners/mongodb
           mv langchain-google/libs/genai langchain/libs/partners/google-genai
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-google/libs/community langchain/libs/partners/google-community
-          # mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
-          # mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
+          mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
+          mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
           mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
           mv langchain-elastic/libs/elasticsearch langchain/libs/partners/elasticsearch
           mv langchain-postgres langchain/libs/partners/postgres
@@ -101,6 +133,12 @@ jobs:
           mv langchain-ai21/libs/ai21 langchain/libs/partners/ai21
           mv langchain-together/libs/together langchain/libs/partners/together
           mv langchain-experimental/libs/experimental langchain/libs/experimental
+          mv langchain-milvus/libs/milvus langchain/libs/partners/milvus
+          mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
+          mv langchain-databricks/libs/databricks langchain/libs/partners/databricks
+          mv langchain-ibm/libs/ibm langchain/libs/partners/ibm
+          mv langchain-azure/libs/azure-dynamic-sessions langchain/libs/partners/azure-dynamic-sessions
+          mv langchain-mongodb/libs/mongodb langchain/libs/partners/mongodb
 
       - name: Rm old html
         run:
@@ -119,8 +157,8 @@ jobs:
         run: |
           python -m pip install -U uv
           python -m uv pip install --upgrade --no-cache-dir pip setuptools
-          # skip airbyte and ibm due to pandas dependency issue
-          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
+          # skip airbyte due to pandas dependency issue
+          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte" | xargs -I {} echo "./libs/partners/{}")
           python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
           python -m uv pip install -r docs/api_reference/requirements.txt
 

.github/workflows/check-broken-links.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   check-links:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

.github/workflows/check_diffs.yml

@@ -3,7 +3,7 @@ name: CI
 
 on:
   push:
-    branches: [cc/test_13]
+    branches: [master]
   pull_request:
 
 # If another push to the same PR or branch happens while this workflow is still running,
@@ -27,12 +27,12 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: '3.11'
-      # - id: files
-      #   uses: Ana06/get-changed-files@v2.2.0
+      - id: files
+        uses: Ana06/get-changed-files@v2.2.0
       - id: set-matrix
         run: |
           python -m pip install packaging
-          python .github/scripts/check_diff.py libs/core/langchain_core/__init__.py >> $GITHUB_OUTPUT
+          python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
       lint: ${{ steps.set-matrix.outputs.lint }}
       test: ${{ steps.set-matrix.outputs.test }}

.github/workflows/people.yml

@@ -14,7 +14,7 @@ on:
 
 jobs:
   langchain-people:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     permissions: write-all
     steps:

.github/workflows/run_notebooks.yml

@@ -20,7 +20,7 @@ env:
 jobs:
   build:
     runs-on: ubuntu-latest
-
+    if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
     name: "Test docs"
     steps:
       - uses: actions/checkout@v4
@@ -35,23 +35,15 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e libs/core
-          pip install -e libs/langchain
-          pip install -e libs/community
-          pip install --upgrade langchain-experimental
-          pip install -e libs//partners/anthropic
-          pip install -e libs//partners/chroma
-          pip install -e libs//partners/openai
-          pip install -e libs//partners/mistralai
-          pip install jupyter langgraph click pypdf vcrpy
+          poetry install --with dev,test
 
       - name: Pre-download tiktoken files
         run: |
-          python docs/scripts/download_tiktoken.py
+          poetry run python docs/scripts/download_tiktoken.py
 
       - name: Prepare notebooks
         run: |
-          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
+          poetry run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory ${{ github.event.inputs.working-directory || 'all' }}
 
       - name: Run notebooks
         env:
@@ -59,5 +51,6 @@ jobs:
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          WORKING_DIRECTORY: ${{ github.event.inputs.working-directory || 'all' }}
         run: |
-          ./docs/scripts/execute_notebooks.sh ${{ github.event.inputs.working-directory || 'all' }}
+          ./docs/scripts/execute_notebooks.sh $WORKING_DIRECTORY

.github/workflows/scheduled_test.yml

@@ -10,7 +10,7 @@ env:
 
 jobs:
   build:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: Python ${{ matrix.python-version }} - ${{ matrix.working-directory }}
     runs-on: ubuntu-latest
     strategy:

MIGRATE.md

@@ -1,70 +1,11 @@
 # Migrating
 
-## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
+Please see the following guides for migratin LangChain code:
 
-In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
-This migration has already started, but we are remaining backwards compatible until 7/28.
-On that date, we will remove functionality from `langchain`.
-Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
+* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
+* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
+* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
+* Upgrate to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
 
-### Migrating to `langchain_experimental`
-
-We are moving any experimental components of LangChain, or components with vulnerability issues, into `langchain_experimental`.
-This guide covers how to migrate.
-
-### Installation
-
-Previously:
-
-`pip install -U langchain`
-
-Now (only if you want to access things in experimental):
-
-`pip install -U langchain langchain_experimental`
-
-### Things in `langchain.experimental`
-
-Previously:
-
-`from langchain.experimental import ...`
-
-Now:
-
-`from langchain_experimental import ...`
-
-### PALChain
-
-Previously:
-
-`from langchain.chains import PALChain`
-
-Now:
-
-`from langchain_experimental.pal_chain import PALChain`
-
-### SQLDatabaseChain
-
-Previously:
-
-`from langchain.chains import SQLDatabaseChain`
-
-Now:
-
-`from langchain_experimental.sql import SQLDatabaseChain`
-
-Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out this [`SQL question-answering tutorial`](https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#convert-question-to-sql-query)
-
-`from langchain.chains import create_sql_query_chain`
-
-### `load_prompt` for Python files
-
-Note: this only applies if you want to load Python files as prompts.
-If you want to load json/yaml files, no change is needed.
-
-Previously:
-
-`from langchain.prompts import load_prompt`
-
-Now:
-
-`from langchain_experimental.prompts import load_prompt`
+The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help automatically upgrade your code to use non deprecated imports. 
+This will be especially helpful if you're still on either version 0.0.x or 0.1.x of LangChain.

README.md

@@ -38,7 +38,7 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/platforms/).
+- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/providers/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
@@ -119,7 +119,7 @@ Agents allow an LLM autonomy over how a task is accomplished. Agents make decisi
 Please see [here](https://python.langchain.com) for full documentation, which includes:
 
 - [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
-- [Tutorials](https://python.langchain.com/docs/use_cases/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
+- [Tutorials](https://python.langchain.com/docs/tutorials/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
 - [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
 - [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
 - [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.

docs/Makefile

@@ -18,7 +18,7 @@ for dir; do \
     if find "$$dir" -maxdepth 1 -type f \( -name "pyproject.toml" -o -name "setup.py" \) | grep -q .; then \
         echo "$$dir"; \
     fi \
-done' sh {} + | grep -vE "airbyte|ibm|couchbase|databricks" | tr '\n' ' ')
+done' sh {} + | grep -vE "airbyte|ibm|databricks" | tr '\n' ' ')
 
 PORT ?= 3001
 
@@ -82,9 +82,9 @@ vercel-build: install-vercel-deps build generate-references
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
 	mkdir static/api_reference
-	git clone --depth=1 https://github.com/baskaryan/langchain-api-docs-build.git
-	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
-	rm -rf langchain-api-docs-build
+	git clone --depth=1 https://github.com/langchain-ai/langchain-api-docs-html.git
+	mv langchain-api-docs-html/api_reference_build/html/* static/api_reference/
+	rm -rf langchain-api-docs-html
 	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
 
 start:

docs/api_reference/create_api_rst.py

@@ -533,6 +533,7 @@ def _build_index(dirs: List[str]) -> None:
         "airbyte": "Airbyte",
         "aws": "AWS",
         "ai21": "AI21",
+        "ibm": "IBM",
     }
     ordered = ["core", "langchain", "text-splitters", "community", "experimental"]
     main_ = [dir_ for dir_ in ordered if dir_ in dirs]

docs/cassettes/agent_executor_114ba50d.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_1f5932b6.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_550e0c6e.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_688b465d.msgpack.zlib

@@ -0,0 +1 @@
+eNqlVmtsFNcVXnDaEJSGNK2gUisyWaEWAbOemR3vw64T1muTGD/W9hq/iLXMztzdHe/szHge+zBxG9y0/EgbaeKa0KSlSbC9yDg2yA5QwECV4lRqUx5JaOwAjRQUFzUVAlKXJKXpmdk12IE/VfbH7Nx7z/nuud/5zrnTk00iReUlcdEwL2pIYVgNBqrRk1VQp45U7ZnBBNJiEtdfFwg27tYVfmpNTNNktbiwkJF5hyQjkeEdrJQoTJKFbIzRCuFdFpAF0x+WuMx0QXqrPYFUlYki1V6Mbd5qZyXYS9RgYG8Glx+omBZDWAox8KdgvIgFNzxmX4fZFUlAppGuIsXe3Q4zCYlDgjkVlTWcNm1EGJHwr2oKYhIwiDCCimBCQwkZDqTpiolBONzmnCQJ+Ri0jGxhR3TROrOJdeu9GNtqF5mEZaAxSV7IhFTEKGwspCBVFzQ11KHmXDiksgov573sPixnhyExyosIk2AlwXchDotICmZSo6AYElU+CSEyLKtDgOabyGGaoqsaGOZ3cGCbVBTRBcsxBT5YRtIxEYGFJoGDmgKqrBSZTGNMWNI1DPAUIBZDSXgCRKUow6wak3SBw8IIY+bCA0cl4zAPIDMKHBRSr1qnlhVIqaLxKDe07Ky3Lx10Po4ZkCBJcUyXTcQ5ZiEjvBi1d3ebmQQ18QriTO7zoO3zTKVwB2I1MAXb/yM1AiNG1QSvxfLZuUtCgrk4TRJ5EZ4JxlzJ01UN/kHT34FtAANGzNzB6C2TdRb/CcgRBnIEwfKgWpDTI1+BxC+zZ0pfQUAb5E/5ylS2d2djiOEgqIu2B/tjkqoZIwtrdhQUiKCQkMhKHGxgvBbt4uV1GIciAuhyCOpURBbtxlAcIRlnBBDuYM7L2MfIssCzFqGFZj0M5+saN2O5c3nILGMcuoCoGeMBCMJXWViXgeYiYqSD9jiIfWlc1RheFKBZ4GZujUHZWj8yf0Fm2DiA4PnGZQzmnEfm20iqMVDDsIHgAkhTCcYAoyRc9Nj8eUUXoUqRkfXX3bldfvH2dk4HSTrc+xcAqxmRNQasznNwgTMkM4OzEmAYrxCDLKSZR8bUtVCIjYTCidKmuL9Sc9VzZZ26P8lVNZEbQx5ORg6q1lHVGCpTGlKVXd7mSGtzTRwn3ZSXIN1uF4mTDsJBOkjc06xwrcFwp4OuaAk5qIpmp49u8jvLwi3eIl8F1RWq3egSa8iMolBVQroz0lTeRlFhz6aWdLrK566tLdIy9Y+nWQ8dIBzJCsrT5tPjHl8JBtHpSZ4rZSl/OFSPGjJP0F2BIiIq02UeV4p3KtVtHOH38rFoeVxS/USUrpgXXhHlwYl8hC6C9hDmb2ROGwJ0Ri1m7PZ4PHug1clQaugng0CZpqs9/aBD9Oc/ZvPXxauBqtsSXt5fDpo0Jhp1aJhkERZgNYwiKBojPcU0VUzT2OM1jcP+/DaNd5Xg/kYFOmcEZFgxJ/ksG9PFOOKG/HcV+4QpdsikGT5cRzhKy5KK8HxUxnAL3pC7KPHK8rFcZeGSEmVEvsva1piwVJ/qSqc4Vue4WDKVILxdtJMPI52NjOddoGOY20BAeEI1+skip3skvzQnvCE4LIGTBE6Qh9O4eW8IcLUAodYzf12DbxGwfehOA02Kw71jDFjpODZ/XUEJ0Ku59W0Q2uv1Hr270RyQ2+t1uw4vtFHR/EhIKqEeutMgD9BPehLqcHrOHuc5Y2oVDEIczbk93jAi6QiBKJIiIpzbzTldbq87TEc49DuzG7KAY+ZSlhQNVxFcfLyWMabWJZi02WJKnUChC05aAj2VFXQOBfVwuWSeQS3B4BIWJIYb9W/A/QwbQ3jQkp+RLW+t9dVU+g+04PN1hAfk3FdRVpRUkY9EBoNIgbQYQ6wg6Rz0SgUNAlaDr9UY93BOMuIsClMIsZ4IcuNl0IXm0G6prt9stFlGgNiTrDEWc5baQcFOewmWYEo9LpogrG+nbYO5zn9y0dsPP7vEZv0KhIYaaXr9gxOfN//8o+o1Jxb1SMYS+wuzp5+cfqi1919bdoxEL1zQ2565caXvG+/Vblm2992j3Vfe+usji9f/hlm858C3G+tfPPLmzJPLx3cWXr8qf0btenT53hv/uMR/unL02n0PdPys+/39S1ecW4GePSufbqrdp1Rvi797Rvjhw89Fxvjztp++9d4740fvT/079cbl1Y86DzStnd15YnSJ7YPT53Xx+auPVW/xHCpfOrtLmey/ec/Tf3mxuOfSxOy2VR+8XNDXd6Ihu7aPLMVa4g0Vk9pkVv37xJGvG+Tl8is/Gv7+qVVTb1R980+nPu0N/XLZJ6/NpJ6/+EJN2UsbG9/fufLiZG8gYlvdSJ9ff2X64+CpS++49I/Xfuupw9ebO2xPOEaX7qhr7tn+um34auCkeHzynnN71sy0LP4PWro5+fLXjvWeOGus/mdf+4dtz93wffKdC13HZ6dn0sEMNXNs14dvJusmPxoYk/4b/Fvn7orPD6+8VnPzJlm+4uDkvXuX9U6enXr1fn7f8daOqfqnftxwUiwo0b73299vKfjVyjj13fOnq5wP9L+0KXYwYEwr5+q314/+If725oey1OuXmtecab3v5sV7HWd/sePX7Ze1I0NnyM+W2WxffFFge3p2+/VXCmy2/wE4OX83

docs/cassettes/agent_executor_77c2f769.msgpack.zlib

@@ -0,0 +1 @@
+eNrtHctu3Na1QYEuDBRoFwW6vB20UBKIHL7mJcMoRiPZUWRZtiVLfsggOOTlDD18mZechwwBbfrYD7rptrUsFYbrJEiQpmndRVdd9AecRRf9hK676LmXpIZja2Ln0TYeMnDk4X2ce97n8pzj0TvHfRwQy3Nfe2S5IQ40PYQH8qt3jgN8N8Ik/PmRg8OuZxxe3tzavh8F1tM3u2Hok6VyWfMt3vOxq1m87jnlvljWu1pYhs++jRmYw7ZnjD795of3Sg4mROtgUlpCt+6VdA/OckN4KN3wIqQFGGmoi23fjGykEWKRUIPpRVQKPBvTZWREQuyUDhbR1O4BHEhQ2MVogDX4K0CWi4j54+zWiOCgdHAbRhzPwDYd6vghp9A1LjyJ8DcJA6w58BAGEYZnOMsHZoRRQEEIfI2OeZ6d4B+OfAbajFzGLwrq5PMSoOhqDlsQan3LHqkEa4HeVQNMIjsk6h0SbzEw0QPLT3aVmiheh7DbsVyMPJhxrH1sINMLEGVrgLvYJVYfUNR0PQIE6SfXoGgDfwyUnMCjawRTXtKNA9iDRsBmF8OK0IMNZACcYuKlUkJa24tCBPACYCvCffgJINZcH0ZJ14tsA7WphBL0YGMw4ikBvhYAoaA2hFHtB6AOQWjh+JGtY5+eITQLhyJke14PRT6FmHIWBGK5ndLBARUkaKIVYIPyPgF6O7PUa9/BeghLmXK8tGhsze0Qxwq7iXROEchWjCdlouXCT0ejMwm7LsL+LbqfR+dhgeaOnuPoyZJFxn8HZIRAG0FfLVBaUKcffAkmPss9qvkBBraB/IIvzcrbB8ddrBmA1D++8Z3DrkfC8eNpe38XNBCDHWFX9ww4YPz7zr7lLyIDmzbo5UOwUhczto8f9jD2Oc0GxT2Kd43f03zftnTG0DK1h0eJVXMUl+enH1Ir5sCDuOH4w01AorlWvjwCx+QikVfqvPDekAOnYbk2OBqOynZ85LP5P2UnfE3vARAucXrjo3jz4+waj4wfbGj65tYUSKoJ4wda4FSVD7LjQeSCleLxcevy88clk5PjZF4U+dr7U4DJyNXHD0zNJvgPU5tBmCNO9wDG+DfCkQ5itvD46b9UVTfVtnNup9daC6tXjOW7UatvrO+Ib6t1w8e8dIlf31aXg6uDtf3Grnljd6PHiTWpIYi1WlXkRF7gRV7k6ruBcWOrfZdXVq+rvLS6KzeVnZa83L7eqDRXpX310ttVd0McBYG0bg/vmjsrNyWpXb92fThcb9YuXaqEoysXhnpd2RT4/qpUv9mMevXmWQTYRX3LOKdLrbZ6BV8dvaXsb1aEjq8s16sDSw4u3jSEVsPqdlZ6HmkJHWU1g15FqnNCgmFVUOoC/e9xqhs2eMawO77fUOq/A1fng6nhnx0By8KIvHMIeoj//rfjJNT8dnN9osLfO1wBnRw/2abeXaygTT1EkiApSKwvKfKSIKALG9uPWskx21QFn6IQD8My84ZcHB/OIghwAcHhuSg0ufr72wG4UhP0cjW1gWO9G7k9bDxsnar9T6j2g2gpPRCeODz0PYK5BM3xo+vc1TjqcmsrH8SmxnlBR3OtfWYK4yfMDAb7w4GhR4bR7Q8cobGvyFYbR7r5YbIFXAg9BhDiHDK+X2vUHyczqSI+BOIFThQ4QfxkyNE4YkOoAQazn0noJ+PDCnD/4+cXhF4P4tD4ARPPX7LzAXZAf+nJEyBKo9H48+mLUkCAotL4ZHoN8DkDRJQc8vHzC1IAoig75NEw3cBZxvjpD+FBlWVFrghG1RA1s1HHstDGhiS321IFV4EJ8h+pe9QBEJWl7wUgawyR0ApH46eLjjakPuecLFbkKpB6FpysbkcG3oraKx4lgpxFEJVtTzPebZ3nWprexdwW08fx8cqNS82NtdZH17msYnGbzIXDvOsR1zLNoy0cgFzGD3XbiwxwngE+AlhXmzfGH9YNWTTltqk3RKNu4hq3vLl1rNmAZF8ff9CVz5WWFEUunUWOdq5eVQSB3bh+ehT7/E+/+4ahhRoNHha4/BK9nulwOeOaax13OwrvOm4nuHIh3NheX3UvCFf98zW5MyotpnEg3sFPLnQ8U29YoIM5hDSMpJZbayym16v4dsUJVVGGlfHFTTUBHRz4gBWAdSPbBhhdz9JplIMLleUaeFhaEhYhytmhVlq6l1zfSpnb4OTiFwOgEVTVNdt+FkZMLEyoO/tv7e7cUfrhzZv25ZFldIM7YrRcXwVgcczL3BMy14T0lvCZ97eSFnQih96VYCHEzNuLEF7NiGh2jODBYsn2OmCLbZJiDFywCIWjUQjxqtsHZ868ulKaKYMsM7OMurfnUl4VzDmNOahgzUzW7JUK5sxiTnyTL9gzgz178H/BnMKwPj9zkpxIwaCZ2pPk3QoOzeSQVVx4ZjNnSyu4M5s75+GlVLeI7hU8mh3bixeK2dw5yAtrXkxjlnkZWm+tbF5avX3mzOyC15NsvStOCLB6V5wUiEtcr30b+O5bag+PWNmnb4+4cHulUmtuXe2t4uXo7uaN+tCorK+HeLilCBu9SzWao0+z++lFI1vGAteYcQC0fKUN0xQEbJEW00KKamA/7FIomtGH9cBomEsyVaoR56loxp+m+vHw1OF0dVwbglGWj85MBNpAnRTenp21nKS4xyZOage/fHiN5iubLG3/UZyPP8nplSVehj/vNuM6wurpdYSjeHp8/83ymzOyqo/T9NrFJD0sVRvTudznygmfJ3v8ixnHviClLNOUcprbe0AresNnET0UKw3xBZhOJ/S+VTupCL2EypRMz7a9gRr56klpKrWOVNDxUyq/WyyJlagYLXRaIcvC7c4+IwqoEac2MhgM+AQhai7UTrKJu9K9BduLqVsAP7NA02zwYWEK6MIiWghwJ16z0AJmm17gWhodZ+WQYEQnrrkWLXnSbCsmyDNR08EBsI4uA82BJXKNr9Uq9JGB4kRJ4hWxLsNIuK9aBoWSbCpf9IjaBKdjY8IAAJY2rd2o2Pf0LqxMXZcoZacpCCp1mksHJRDFJUVcOKCIxtJhVNoaCUEIBvWBz8ITBIHhO1nxPEhZoCjRmrSq050KL6TPJjxXajwlySKqoVHOiIxPrmGdsJlWMijYDYuEFJSlx7wtl3XDfVZeyWO5qgyrShlAlkVF5n23E/PfoDSLgixQKgfgiVXHp/TIvJIO9NhAZTJg4E6A6T5JFk7GrIBJfpeC9UHpSBRg1Wkz6GKdkXgybFF0ZYEXavGobvmq4yzQ0rwwGWGr4pFu5FjAAMqOBt3DcusMNJ01MbaJbfVwzE+Zr08NMqZW2SBFVe9atp1dORnMrARvEbK4lJXRZDAjKAMPfA8iWwpSyY4lEKkM+yDRnsOQZsDos2NRBV1CVTYS9Rnj6eJOBBoUS0LhK+lALIkaLx0c0OCqe7SfQeAbDVDjah1sPevV46T1xOinjBJxKHUC5wGKDgqLNjSfwPgO7I5Ib3SqM+gnk3HI1FzOzPiOiV+YPuufv/7JyWlmehotw4sCAoUki1P1eDPwHEQr6F7gwSUArNlG1KtTd7cI7rGtaw58IJEbWASzfgn4THAIkyAcNhBrkBXvQg4ljJ7HKvW+rekY8TxP3byDtsHs9z0XL6FTfAd6/dp2i6u9gcqolfhn6iZALRUwYxYgyqIAFoX4kwUppWcnnD2LtiKXIbbhAT5w9rT4qg25WqmeJr/YgbPYptKTS0sSX60cfJX9PGe+/zXo55neyKLYZMnUkdlL86zmHXrfRS8qGX2hHp/MbRyV7u3F0XsPHvZeGMD30uv7NI9uARSwsxjGZ8TdPTh+L90Yry6ibxF9i+j7/4i+e8xjzTDc2THyFBuejpR7kVQVhCJYTgdL4PbtbMigIWAqFKgv4/CLntGiZ7ToGS16Roue0TnoGT2UKnXlq20aVea5aVSRK1//ptGqIn2ZplGpWj+9abSqtau6UcGi3hBN3DYlUW/XRCxKdUlQavVXomnU0JU6WMbnbxpt/3t2Feh6bUPaiEa9u9bO2h3Rvyp2l5fPi/ubvS9UBYJ3wf9h02ip9N9o0Py6c2RC/nYXl3JGMjrp38kZ3SddOTmjm/ba5Ixk1kGTM5ozfTH5onwxd6KeJKALWc+7rOOaQv48OKuh5C9W549kLX8kxy1IuaN7aS93tzIud7q9nan45E2/cyfsvBEsKnmjmM8bwULeCP7re63c+a3X80ZxpVY4rjknWM6f4zqfN5LfKN6g5j847ea0TtVKG4SL98Z5FzXt+s4b0YXrzoHrhun8lWp8nL8CVZHrK96nijfmV5vg3CVzkeN3i46Rue/v9Bz271TyVlQPPCd3RIc57F8mXhR2Bzh/b5CoeIWcfxlfTv6BefFCVbxQzVnzhCDmjeR68Q5ZtIvM2ztku8jcF9euedPqt6LcvTyybyMqrpnFNXO+CG7krtXtR0WAmn8z3rGI1bbswmUXLnsOMwPFe3LxnjxnRtxz8veenL+LyGUbqMpfDc71QpzDYqsW5pDoHH4nQeZbSouvoJh/kiM/f2adu6+JQvSr7ItvV5l/kj2zSBjMe8JALJJg895+n7+MAfsVLvm7idBfyJS7GOUWMWrOCZYEqfgHNUUZsqjUFCJ+tURcKYpx80jwS/w2cBJ6/mm/B/w/5izgpg==

docs/cassettes/agent_executor_ae627966.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_af83d3e3.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_b6a7e925.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_c4073e35.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_c96c960b.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_executor_e593bbf6.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/binding_2b65beab-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/binding_32e0484a-78c5-4570-a00b-20d597245a96.msgpack.zlib

@@ -0,0 +1 @@
+eNptVX1sHMUVP5OiOm2RqBCRIvVje7gVpN7z7t3eee8cI/wd2zhn+85N4phYc7tzd5Pb3dnszN5XlNCYFgqISCvRBFJASny5a69OsEka0hRbRSECEUpoWiEMbUQrUIVaRNT80SqEj9nzmdhK9o+7nTdvfu/33vvN26lKFloEYaNhBhkUWkChbEGcqYoFd9mQ0J+VdUjTWC0NR2PxadtCixvSlJok0tICTOTDJjQA8ilYb8mKLUoa0Bb2bmqwBlNKYLXwbkN0t1eHhIAUJN4It323V8EslkHZwrvFQhRy2KYcTUMuiTUN55CR4lh04GJwNnGXQEvBhAWQwpGCnsAacd0NjmAtCzlEfdwYgXUESwd0wpgwekbGOuL90c0Rn883YcSi949dX00Y3mbOa2ENuhxIgVCoe/c0c6uo5TkWkECVo7gGTdPIUjlTswlHYJZFdzkyJqJ/JZhNoOXd8wCz6FiFmmtKmZQP+II8ta0Edn0NZhXZP6HYdB2WyXlrNgsCnVmTDBsyA6Nmssawwy6+4BP2VNIQqKxtlzy3l9KYUOf46lY8DxQFspjQULDKquccSxWR2cypMKkBCqssRwPWGu1UMxCaPNBQFpaXTjmzwDQ1pNSq37KTYGOmXhOeFkx443bVzZhnzTWoczLKSHT0twwXmGYMTvRJsk+YzfOEAmRoTAO8Bhifslnb/8PKDRMoGQbC1/XolJcOH1/pg4lzdAgo0dgqSGApaecosPSQdGKl3bINinToVLqGbwxX37weLuATRV/r3CpgUjAU52itES+uOgypVeAVzDCcw0JZwTiDoLP438lJJTmZ0NuHbTw0LPemhUJs2FRQvm+obwwkY1AIbgGddCCTTcX9PsPsmUxneLHVHxZEWRQkXvQJPtEn8vEiUIeknZs6EqQQFzKdZlxU5ZGYJu3qHCz05/oGwmhoa/9k3I6FRwfz23IdPX57rJd2wq0IxYrjRJQoao0LW8b1PnkT3FQoCOOgO9XGMXZ2FqntcjETHFEzvWEUMrHW110UcoEBKPQRnLZNLBcQpOObW2E0qY6soBcKh3mhzjAkSLLgPseXtaFBI0XTznQgEPy1BYnJbj98qMxKRm0yVWI6hG+8VqlPgSPRwesSXlfqZpp05uM2E7sY5KIK5fyCX+JEOSLJEX+Q6xuKz3TVw8RvKsG5uAUMkmQy7FmWfEVJ20YGqtWum4p93hU766RLn91cHuZNTCBfZ+XMbOVHl+Yf3999Yulm8dhKAQMVa2Gd+Zrqc8V8TlVsVU1nc7oQLkoBlIC2kjxZP2Ja2A3DCPE6caalVuF4fWdZd1WWq8CLAi+IZ/I8u+ZQQzpi9az91ocwcUpBVuzTNzpQnIFsXFekWjeEhZUeFtSZYN3Y12GkcDj80s2dlqECzCUs+c+s9iJwJRvRr5PTNzrUIY4IZCa/7M0j1VlsYovJcCAh+MMiDKtCQBYkMQjDYqAVggCQoSpC8fds9CGFobjNNLFFeQIV9sWhBWexWQd5d8a0B8RgIMQybeOQoWi2CmN2ohu7OZA2zrSghoH6fFcv3wWUNORjNf05le5tmzuG+rtObeVXComPmktfu4qBiYGSyXIMWqwxTlXRsK2yYWnBMsMa7djmnJTVgJhMJPyJsNgqKVDmO9kYWkb7SnYld9JWgMa4ZxXnRDrQ7o1IUsDbxumgXQ6xNtW+ifvKbq5G6lwD+v7jjZ7as0YbPr/5rHD7S//68U8WLh6R+du6/znc9LV2T/Wnay68bp/6zctz/O677jz8edvAoVDPx+ceWX/hP29c6/Xc/2Hzmh8dnFI/og9uvGfKWts++kJ1x3MXf/XR5Z0vvzr93pa9B1/YcWz2SsuTJ8eeHdgeeaI0s/jWxWlu5oHCoYlXrj3z6tmH79b3nzlw5/6rD/3lxL1je41Pxeib4x9e3PDoPnh5XYPnWgg/++cPTifWvzLPjyYaj9x34ON7GwRsPyF8e+obTd+a29E3kGkcuuPT4tWNew9t/Ftq9up3/ri2ZK7dWGj9JDn5/q6RW0e+Hvrl2/zCpR++dcq89GbX+U8u/9y/oTn1vZ7Z8sK61yJvz/678f1pNP7ZNy888+LR6fWLWPrdP4S/vzNnvLf/g7MHmgY7c///xf/2n/6T1XSYvrP9sf4fHHwq97r87m+z+StPnSs/5z+3b/CvkbnLdzSeX+x8+pb5K9XFhaet737W4PF88cUaz7EDHeuLt3g8XwL1DYCO

docs/cassettes/binding_f3fdf86d-155f-4587-b7cd-52d363970c1d.msgpack.zlib

@@ -0,0 +1 @@
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

docs/docs/concepts.mdx

@@ -611,7 +611,7 @@ Read more about [defining tools that return artifacts here](/docs/how_to/tool_ar
 When designing tools to be used by a model, it is important to keep in mind that:
 
 - Chat models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
-- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This another form of prompt engineering.
+- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This is another form of prompt engineering.
 - Simple, narrowly scoped tools are easier for models to use than complex tools.
 
 #### Related
@@ -713,7 +713,7 @@ Callback handlers can either be `sync` or `async`:
 * Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
 * Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
 
-During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
+During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
 
 #### Passing callbacks
 

docs/docs/contributing/index.mdx

@@ -3,8 +3,8 @@ sidebar_position: 0
 ---
 # Welcome Contributors
 
-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.
+Hi there! Thank you for your interest in contributing to LangChain.
+As an open-source project in a fast developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
 ## 🗺️ Guidelines
 

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "## Dependencies\n",
     "\n",
-    "**Note**: this guide requires `langchain-core` >= 0.2.13. We will also use [OpenAI](/docs/integrations/platforms/openai/) for embeddings, but any LangChain embeddings should suffice. We will use a simple [LangGraph](https://langchain-ai.github.io/langgraph/) agent for demonstration purposes."
+    "**Note**: this guide requires `langchain-core` >= 0.2.13. We will also use [OpenAI](/docs/integrations/providers/openai/) for embeddings, but any LangChain embeddings should suffice. We will use a simple [LangGraph](https://langchain-ai.github.io/langgraph/) agent for demonstration purposes."
    ]
   },
   {

docs/docs/how_to/custom_tools.ipynb

@@ -22,7 +22,7 @@
     "2. LangChain [Runnables](/docs/concepts#runnable-interface);\n",
     "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
     "\n",
     "In this guide we provide an overview of these methods.\n",
     "\n",

docs/docs/how_to/installation.mdx

@@ -50,7 +50,7 @@ pip install langchain-core
 ### Integration packages
 
 Certain integrations like OpenAI and Anthropic have their own packages.
-Any integrations that require their own package will be documented as such in the [Integration docs](/docs/integrations/platforms/).
+Any integrations that require their own package will be documented as such in the [Integration docs](/docs/integrations/providers/).
 You can see a list of all integration packages in the [API reference](https://api.python.langchain.com) under the "Partner libs" dropdown.
 To install one of these run:
 

docs/docs/how_to/migrate_agent.ipynb

@@ -34,6 +34,12 @@
     "LangChain agents (the [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
     "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
     "\n",
+    "\n",
+    ":::note\n",
+    "In LangGraph, the graph replaces LangChain's agent executor. It manages the agent's cycles and tracks the scratchpad as messages within its state. The LangChain \"agent\" corresponds to the state_modifier and LLM you've provided.\n",
+    ":::\n",
+    "\n",
+    "\n",
     "#### Prerequisites\n",
     "\n",
     "This how-to guide uses OpenAI as the LLM. Install the dependencies to run."
@@ -183,10 +189,10 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools)\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "{\n",
     "    \"input\": query,\n",
     "    \"output\": messages[\"messages\"][-1].content,\n",
@@ -216,7 +222,9 @@
     "\n",
     "new_query = \"Pardon?\"\n",
     "\n",
-    "messages = app.invoke({\"messages\": message_history + [(\"human\", new_query)]})\n",
+    "messages = langgraph_agent_executor.invoke(\n",
+    "    {\"messages\": message_history + [(\"human\", new_query)]}\n",
+    ")\n",
     "{\n",
     "    \"input\": new_query,\n",
     "    \"output\": messages[\"messages\"][-1].content,\n",
@@ -309,10 +317,12 @@
     "# This could also be a SystemMessage object\n",
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=system_message)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=system_message\n",
+    ")\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"user\", query)]})"
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"user\", query)]})"
    ]
   },
   {
@@ -356,10 +366,12 @@
     "    ]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "print(\n",
     "    {\n",
     "        \"input\": query,\n",
@@ -503,13 +515,13 @@
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
     "memory = MemorySaver()\n",
-    "app = create_react_agent(\n",
+    "langgraph_agent_executor = create_react_agent(\n",
     "    model, tools, state_modifier=system_message, checkpointer=memory\n",
     ")\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
     "print(\n",
-    "    app.invoke(\n",
+    "    langgraph_agent_executor.invoke(\n",
     "        {\n",
     "            \"messages\": [\n",
     "                (\"user\", \"Hi, I'm polly! What's the output of magic_function of 3?\")\n",
@@ -520,15 +532,15 @@
     ")\n",
     "print(\"---\")\n",
     "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"Remember my name?\")]}, config)[\"messages\"][\n",
-    "        -1\n",
-    "    ].content\n",
+    "    langgraph_agent_executor.invoke(\n",
+    "        {\"messages\": [(\"user\", \"Remember my name?\")]}, config\n",
+    "    )[\"messages\"][-1].content\n",
     ")\n",
     "print(\"---\")\n",
     "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"what was that output again?\")]}, config)[\n",
-    "        \"messages\"\n",
-    "    ][-1].content\n",
+    "    langgraph_agent_executor.invoke(\n",
+    "        {\"messages\": [(\"user\", \"what was that output again?\")]}, config\n",
+    "    )[\"messages\"][-1].content\n",
     ")"
    ]
   },
@@ -636,9 +648,13 @@
     "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages()\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
-    "for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
+    "for step in langgraph_agent_executor.stream(\n",
+    "    {\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"\n",
+    "):\n",
     "    print(step)"
    ]
   },
@@ -707,9 +723,9 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "\n",
     "messages"
    ]
@@ -839,10 +855,10 @@
     "\n",
     "RECURSION_LIMIT = 2 * 3 + 1\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream(\n",
+    "    for chunk in langgraph_agent_executor.stream(\n",
     "        {\"messages\": [(\"human\", query)]},\n",
     "        {\"recursion_limit\": RECURSION_LIMIT},\n",
     "        stream_mode=\"values\",\n",
@@ -953,12 +969,12 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "# Set the max timeout for each step here\n",
-    "app.step_timeout = 2\n",
+    "langgraph_agent_executor.step_timeout = 2\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream({\"messages\": [(\"human\", query)]}):\n",
+    "    for chunk in langgraph_agent_executor.stream({\"messages\": [(\"human\", query)]}):\n",
     "        print(chunk)\n",
     "        print(\"------\")\n",
     "except TimeoutError:\n",
@@ -994,17 +1010,21 @@
     "\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "\n",
-    "async def stream(app, inputs):\n",
-    "    async for chunk in app.astream({\"messages\": [(\"human\", query)]}):\n",
+    "async def stream(langgraph_agent_executor, inputs):\n",
+    "    async for chunk in langgraph_agent_executor.astream(\n",
+    "        {\"messages\": [(\"human\", query)]}\n",
+    "    ):\n",
     "        print(chunk)\n",
     "        print(\"------\")\n",
     "\n",
     "\n",
     "try:\n",
-    "    task = asyncio.create_task(stream(app, {\"messages\": [(\"human\", query)]}))\n",
+    "    task = asyncio.create_task(\n",
+    "        stream(langgraph_agent_executor, {\"messages\": [(\"human\", query)]})\n",
+    "    )\n",
     "    await asyncio.wait_for(task, timeout=3)\n",
     "except TimeoutError:\n",
     "    print(\"Task Cancelled.\")"
@@ -1108,10 +1128,10 @@
     "\n",
     "RECURSION_LIMIT = 2 * 1 + 1\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream(\n",
+    "    for chunk in langgraph_agent_executor.stream(\n",
     "        {\"messages\": [(\"human\", query)]},\n",
     "        {\"recursion_limit\": RECURSION_LIMIT},\n",
     "        stream_mode=\"values\",\n",
@@ -1289,10 +1309,14 @@
     "    return [(\"system\", \"You are a helpful assistant\"), state[\"messages\"][0]]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
     "try:\n",
-    "    for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
+    "    for step in langgraph_agent_executor.stream(\n",
+    "        {\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"\n",
+    "    ):\n",
     "        pass\n",
     "except GraphRecursionError as e:\n",
     "    print(\"Stopping agent prematurely due to triggering stop condition\")"

docs/docs/how_to/vectorstore_retriever.ipynb

@@ -77,7 +77,7 @@
    "id": "08f8b820-5912-49c1-9d76-40be0571dffb",
    "metadata": {},
    "source": [
-    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html)), which we can use in the usual way:"
+    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStoreRetriever.html)), which we can use in the usual way:"
    ]
   },
   {

docs/docs/integrations/chat/friendli.ipynb

@@ -29,7 +29,7 @@
     "Ensure the `langchain_community` and `friendli-client` are installed.\n",
     "\n",
     "```sh\n",
-    "pip install -U langchain-comminity friendli-client.\n",
+    "pip install -U langchain-community friendli-client.\n",
     "```\n",
     "\n",
     "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -36,12 +36,12 @@
     "### Integration details\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| ChatWatsonx | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |\n",
+    "| ChatWatsonx | ❌ | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | "
+    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | "
    ]
   },
   {
@@ -126,21 +126,19 @@
    "source": [
     "## Instantiation\n",
     "\n",
-    "You might need to adjust model `parameters` for different models or tasks. For details, refer to [Available MetaNames](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)."
+    "You might need to adjust model `parameters` for different models or tasks. For details, refer to [Available TextChatParameters](https://ibm.github.io/watsonx-ai-python-sdk/fm_schema.html#ibm_watsonx_ai.foundation_models.schema.TextChatParameters)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "407cd500",
    "metadata": {},
    "outputs": [],
    "source": [
     "parameters = {\n",
-    "    \"decoding_method\": \"sample\",\n",
-    "    \"max_new_tokens\": 100,\n",
-    "    \"min_new_tokens\": 1,\n",
-    "    \"stop_sequences\": [\".\"],\n",
+    "    \"temperature\": 0.9,\n",
+    "    \"max_tokens\": 200,\n",
     "}"
    ]
   },
@@ -160,20 +158,20 @@
     "In this example, we’ll use the `project_id` and Dallas URL.\n",
     "\n",
     "\n",
-    "You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported foundation models](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes)."
+    "You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported chat models](https://ibm.github.io/watsonx-ai-python-sdk/fm_helpers.html#ibm_watsonx_ai.foundation_models_manager.FoundationModelsManager.get_chat_model_specs)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "98371396",
+   "id": "e3568e91",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_ibm import ChatWatsonx\n",
     "\n",
     "chat = ChatWatsonx(\n",
-    "    model_id=\"ibm/granite-13b-chat-v2\",\n",
+    "    model_id=\"ibm/granite-34b-code-instruct\",\n",
     "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
     "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
     "    params=parameters,\n",
@@ -196,7 +194,7 @@
    "outputs": [],
    "source": [
     "chat = ChatWatsonx(\n",
-    "    model_id=\"ibm/granite-13b-chat-v2\",\n",
+    "    model_id=\"ibm/granite-34b-code-instruct\",\n",
     "    url=\"PASTE YOUR URL HERE\",\n",
     "    username=\"PASTE YOUR USERNAME HERE\",\n",
     "    password=\"PASTE YOUR PASSWORD HERE\",\n",
@@ -242,17 +240,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "beea2b5b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Je t'aime pour écouter la Rock.\", response_metadata={'token_usage': {'generated_token_count': 12, 'input_token_count': 28}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-05b305ce-5401-4a10-b557-41a4b15c7f6f-0')"
+       "AIMessage(content=\"J'adore que tu escois de écouter de la rock ! \", additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 34, 'total_tokens': 53}, 'model_name': 'ibm/granite-34b-code-instruct', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='chat-ef888fc41f0d4b37903b622250ff7528', usage_metadata={'input_tokens': 34, 'output_tokens': 19, 'total_tokens': 53})"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,17 +271,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "id": "8ab1a25a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Sure, I can help you with that! Horses are large, powerful mammals that belong to the family Equidae.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-391776ff-3b38-4768-91e8-ff64177149e5-0')"
+       "AIMessage(content='horses are quadrupedal mammals that are members of the family Equidae. They are typically farm animals, competing in horse racing and other forms of equine competition. With over 200 breeds, horses are diverse in their physical appearance and behavior. They are intelligent, social animals that are often used for transportation, food, and entertainment.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 89, 'prompt_tokens': 29, 'total_tokens': 118}, 'model_name': 'ibm/granite-34b-code-instruct', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='chat-9a6e28abb3d448aaa4f83b677a9fd653', usage_metadata={'input_tokens': 29, 'output_tokens': 89, 'total_tokens': 118})"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "id": "dd919925",
    "metadata": {},
    "outputs": [],
@@ -338,17 +336,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "id": "68160377",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe Python.', response_metadata={'token_usage': {'generated_token_count': 5, 'input_token_count': 23}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-1b1ccf5d-0e33-46f2-a087-e2a136ba1fb7-0')"
+       "AIMessage(content='Ich liebe Python.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 28, 'total_tokens': 35}, 'model_name': 'ibm/granite-34b-code-instruct', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='chat-fef871190b6047a7a3e68c58b3810c33', usage_metadata={'input_tokens': 28, 'output_tokens': 7, 'total_tokens': 35})"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "3f63166a",
    "metadata": {},
    "outputs": [
@@ -384,7 +382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The moon is a natural satellite of the Earth, and it has been a source of fascination for humans for centuries."
+      "The Moon is the fifth largest moon in the solar system and the largest relative to its host planet. It is the fifth brightest object in Earth's night sky after the Sun, the stars, the Milky Way, and the Moon itself. It orbits around the Earth at an average distance of 238,855 miles (384,400 kilometers). The Moon's gravity is about one-sixthth of Earth's and thus allows for the formation of tides on Earth. The Moon is thought to have formed around 4.5 billion years ago from debris from a collision between Earth and a Mars-sized body named Theia. The Moon is effectively immutable, with its current characteristics remaining from formation. Aside from Earth, the Moon is the only other natural satellite of Earth. The most widely accepted theory is that it formed from the debris of a collision"
      ]
     }
    ],
@@ -410,18 +408,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 13,
    "id": "9e948729",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content='Cats are domestic animals that belong to the Felidae family.', response_metadata={'token_usage': {'generated_token_count': 13, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-71a8bd7a-a1aa-497b-9bdd-a4d6fe1d471a-0'),\n",
-       " AIMessage(content='Dogs are domesticated mammals of the family Canidae, characterized by their adaptability to various environments and social structures.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-22b7a0cb-e44a-4b68-9921-872f82dcd82b-0')]"
+       "[AIMessage(content='The cat is a popular domesticated carnivorous mammal that belongs to the family Felidae. Cats arefriendly, intelligent, and independent animals that are well-known for their playful behavior, agility, and ability to hunt prey. cats come in a wide range of breeds, each with their own unique physical and behavioral characteristics. They are kept as pets worldwide due to their affectionate nature and companionship. Cats are important members of the household and are often involved in everything from childcare to entertainment.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 127, 'prompt_tokens': 28, 'total_tokens': 155}, 'model_name': 'ibm/granite-34b-code-instruct', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='chat-fa452af0a0fa4a668b6a704aecd7d718', usage_metadata={'input_tokens': 28, 'output_tokens': 127, 'total_tokens': 155}),\n",
+       " AIMessage(content='Dogs are domesticated animals that belong to the Canidae family, also known as wolves. They are one of the most popular pets worldwide, known for their loyalty and affection towards their owners. Dogs come in various breeds, each with unique characteristics, and are trained for different purposes such as hunting, herding, or guarding. They require a lot of exercise and mental stimulation to stay healthy and happy, and they need proper training and socialization to be well-behaved. Dogs are also known for their playful and energetic nature, making them great companions for people of all ages.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 144, 'prompt_tokens': 28, 'total_tokens': 172}, 'model_name': 'ibm/granite-34b-code-instruct', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='chat-cae7663c50cf4f3499726821cc2f0ec7', usage_metadata={'input_tokens': 28, 'output_tokens': 144, 'total_tokens': 172})]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,9 +450,7 @@
     "\n",
     "### ChatWatsonx.bind_tools()\n",
     "\n",
-    "Please note that `ChatWatsonx.bind_tools` is on beta state, so right now we only support `mistralai/mixtral-8x7b-instruct-v01` model.\n",
-    "\n",
-    "You should also redefine `max_new_tokens` parameter to get the entire model response. By default `max_new_tokens` is set to 20."
+    "Please note that `ChatWatsonx.bind_tools` is on beta state, so we recommend using `mistralai/mistral-large` model."
    ]
   },
   {
@@ -466,10 +462,8 @@
    "source": [
     "from langchain_ibm import ChatWatsonx\n",
     "\n",
-    "parameters = {\"max_new_tokens\": 200}\n",
-    "\n",
     "chat = ChatWatsonx(\n",
-    "    model_id=\"mistralai/mixtral-8x7b-instruct-v01\",\n",
+    "    model_id=\"mistralai/mistral-large\",\n",
     "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
     "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
     "    params=parameters,\n",
@@ -478,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "e1633a73",
    "metadata": {},
    "outputs": [],
@@ -497,17 +491,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "3bf9b8ab",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'function_call': {'type': 'function'}, 'tool_calls': [{'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"Los Angeles\"}'}, 'id': None}, {'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"New York\"}'}, 'id': None}]}, response_metadata={'token_usage': {'generated_token_count': 99, 'input_token_count': 320}, 'model_name': 'mistralai/mixtral-8x7b-instruct-v01', 'system_fingerprint': '', 'finish_reason': 'eos_token'}, id='run-38627104-f2ac-4edb-8390-d5425fb65979-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None}, {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}])"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'chatcmpl-tool-6c06a19bbe824d78a322eb193dbde12d', 'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"Los Angeles, CA\"}'}}, {'id': 'chatcmpl-tool-493542e46f1141bfbfeb5deae6c9e086', 'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"New York, NY\"}'}}]}, response_metadata={'token_usage': {'completion_tokens': 46, 'prompt_tokens': 95, 'total_tokens': 141}, 'model_name': 'mistralai/mistral-large', 'system_fingerprint': '', 'finish_reason': 'tool_calls'}, id='chat-027f2bdb217e4238909cb26d3e8a8fbf', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'chatcmpl-tool-6c06a19bbe824d78a322eb193dbde12d', 'type': 'tool_call'}, {'name': 'GetWeather', 'args': {'location': 'New York, NY'}, 'id': 'chatcmpl-tool-493542e46f1141bfbfeb5deae6c9e086', 'type': 'tool_call'}], usage_metadata={'input_tokens': 95, 'output_tokens': 46, 'total_tokens': 141})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,18 +524,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "38f10ba7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None},\n",
-       " {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}]"
+       "[{'name': 'GetWeather',\n",
+       "  'args': {'location': 'Los Angeles, CA'},\n",
+       "  'id': 'chatcmpl-tool-6c06a19bbe824d78a322eb193dbde12d',\n",
+       "  'type': 'tool_call'},\n",
+       " {'name': 'GetWeather',\n",
+       "  'args': {'location': 'New York, NY'},\n",
+       "  'id': 'chatcmpl-tool-493542e46f1141bfbfeb5deae6c9e086',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +567,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/oci_data_science.ipynb

@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: ChatOCIModelDeployment\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ChatOCIModelDeployment\n",
+    "\n",
+    "This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n",
+    "\n",
+    "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n",
+    "\n",
+    "> For the latest updates, examples and experimental features, please see [ADS LangChain Integration](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/large_language_model/langchain_models.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatOCIModelDeployment](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| depends | depends | depends | depends | depends | depends | ✅ | ✅ | ✅ | ✅ | \n",
+    "\n",
+    "Some model features, including tool calling, structured output, JSON mode and multi-modal inputs, are depending on deployed model.\n",
+    "\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use ChatOCIModelDeployment you'll need to deploy a chat model with chat completion endpoint and install the `langchain-community`, `langchain-openai` and `oracle-ads` integration packages.\n",
+    "\n",
+    "You can easily deploy foundation models using the [AI Quick Actions](https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-quick-actions/model-deployment-tips.md) on OCI Data Science Model deployment. For additional deployment examples, please visit the [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/tree/main/ai-quick-actions).\n",
+    "\n",
+    "### Policies\n",
+    "Make sure to have the required [policies](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint) to access the OCI Data Science Model Deployment endpoint.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "You can set authentication through Oracle ADS. When you are working in OCI Data Science Notebook Session, you can leverage resource principal to access other OCI resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ads\n",
+    "\n",
+    "# Set authentication through ads\n",
+    "# Use resource principal are operating within a\n",
+    "# OCI service that has resource principal based\n",
+    "# authentication configured\n",
+    "ads.set_auth(\"resource_principal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can configure the credentials using the following environment variables. For example, to use API key with specific profile:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Set authentication through environment variables\n",
+    "# Use API Key setup when you are working from a local\n",
+    "# workstation or on platform which does not support\n",
+    "# resource principals.\n",
+    "os.environ[\"OCI_IAM_TYPE\"] = \"api_key\"\n",
+    "os.environ[\"OCI_CONFIG_PROFILE\"] = \"default\"\n",
+    "os.environ[\"OCI_CONFIG_LOCATION\"] = \"~/.oci\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check out [Oracle ADS docs](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) to see more options."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain OCIModelDeployment integration lives in the `langchain-community` package. The following command will install `langchain-community` and the required dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-community langchain-openai oracle-ads"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "You may instantiate the model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.\n",
+    "\n",
+    "* Using `ChatOCIModelDeployment` when you need a generic entry point for deploying models. You can pass model parameters through `model_kwargs` during the instantiation of this class. This allows for flexibility and ease of configuration without needing to rely on framework-specific details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models import ChatOCIModelDeployment\n",
+    "\n",
+    "# Create an instance of OCI Model Deployment Endpoint\n",
+    "# Replace the endpoint uri with your own\n",
+    "# Using generic class as entry point, you will be able\n",
+    "# to pass model parameters through model_kwargs during\n",
+    "# instantiation.\n",
+    "chat = ChatOCIModelDeployment(\n",
+    "    endpoint=\"https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict\",\n",
+    "    streaming=True,\n",
+    "    max_retries=1,\n",
+    "    model_kwargs={\n",
+    "        \"temperature\": 0.2,\n",
+    "        \"max_tokens\": 512,\n",
+    "    },  # other model params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Using framework specific class like `ChatOCIModelDeploymentVLLM`: This is suitable when you are working with a specific framework (e.g. `vLLM`) and need to pass model parameters directly through the constructor, streamlining the setup process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models import ChatOCIModelDeploymentVLLM\n",
+    "\n",
+    "# Create an instance of OCI Model Deployment Endpoint\n",
+    "# Replace the endpoint uri with your own\n",
+    "# Using framework specific class as entry point, you will\n",
+    "# be able to pass model parameters in constructor.\n",
+    "chat = ChatOCIModelDeploymentVLLM(\n",
+    "    endpoint=\"https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", response_metadata={'token_usage': {'prompt_tokens': 44, 'total_tokens': 52, 'completion_tokens': 8}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-ca145168-efa9-414c-9dd1-21d10766fdd3-0')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "\n",
+    "ai_msg = chat.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "J'adore programmer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe Programmierung.', response_metadata={'token_usage': {'prompt_tokens': 38, 'total_tokens': 48, 'completion_tokens': 10}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-5dd936b0-b97e-490e-9869-2ad3dd524234-0')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='我喜欢编程', response_metadata={'token_usage': {'prompt_tokens': 37, 'total_tokens': 50, 'completion_tokens': 13}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-a2dc9393-f269-41a4-b908-b1d8a92cf827-0')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.chat_models import ChatOCIModelDeployment\n",
+    "\n",
+    "system = \"You are a helpful translator that translates {input_language} to {output_language}.\"\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chat = ChatOCIModelDeployment(\n",
+    "    endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict\"\n",
+    ")\n",
+    "chain = prompt | chat\n",
+    "\n",
+    "await chain.ainvoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Chinese\",\n",
+    "        \"text\": \"I love programming\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Streaming calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "1. California\n",
+      "2. Texas\n",
+      "3. Florida\n",
+      "4. New York\n",
+      "5. Illinois"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "from langchain_community.chat_models import ChatOCIModelDeployment\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"human\", \"List out the 5 states in the United State.\")]\n",
+    ")\n",
+    "\n",
+    "chat = ChatOCIModelDeployment(\n",
+    "    endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict\"\n",
+    ")\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "\n",
+    "for chunk in chain.stream({}):\n",
+    "    sys.stdout.write(chunk.content)\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Structured output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': 'Why did the cat get stuck in the tree?',\n",
+       " 'punchline': 'Because it was chasing its tail!'}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.chat_models import ChatOCIModelDeployment\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class Joke(BaseModel):\n",
+    "    \"\"\"A setup to a joke and the punchline.\"\"\"\n",
+    "\n",
+    "    setup: str\n",
+    "    punchline: str\n",
+    "\n",
+    "\n",
+    "chat = ChatOCIModelDeployment(\n",
+    "    endpoint=\"https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict\",\n",
+    ")\n",
+    "structured_llm = chat.with_structured_output(Joke, method=\"json_mode\")\n",
+    "output = structured_llm.invoke(\n",
+    "    \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n",
+    ")\n",
+    "\n",
+    "output.dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For comprehensive details on all features and configurations, please refer to the API reference documentation for each class:\n",
+    "\n",
+    "* [ChatOCIModelDeployment](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html)\n",
+    "* [ChatOCIModelDeploymentVLLM](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeploymentVLLM.html)\n",
+    "* [ChatOCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeploymentTGI.html)"
+   ]
+  }
+ ],
+ "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/openai.ipynb

@@ -434,6 +434,160 @@
     "fine_tuned_model.invoke(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5d5d9793",
+   "metadata": {},
+   "source": [
+    "## Multimodal Inputs\n",
+    "\n",
+    "OpenAI has models that support multimodal inputs. You can pass in images or audio to these models. For more information on how to do this in LangChain, head to the [multimodal inputs](/docs/how_to/multimodal_inputs) docs.\n",
+    "\n",
+    "You can see the list of models that support different modalities in [OpenAI's documentation](https://platform.openai.com/docs/models).\n",
+    "\n",
+    "At the time of this doc's writing, the main OpenAI models you would use would be:\n",
+    "\n",
+    "- Image inputs: `gpt-4o`, `gpt-4o-mini`\n",
+    "- Audio inputs: `gpt-4o-audio-preview`\n",
+    "\n",
+    "For an example of passing in image inputs, see the [multimodal inputs how-to guide](/docs/how_to/multimodal_inputs).\n",
+    "\n",
+    "Below is an example of passing audio inputs to `gpt-4o-audio-preview`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "39d08780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"I'm sorry, but I can't create audio content that involves yelling. Is there anything else I can help you with?\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-4o-audio-preview\",\n",
+    "    temperature=0,\n",
+    ")\n",
+    "\n",
+    "with open(\n",
+    "    \"../../../../libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav\",\n",
+    "    \"rb\",\n",
+    ") as f:\n",
+    "    # b64 encode it\n",
+    "    audio = f.read()\n",
+    "    audio_b64 = base64.b64encode(audio).decode()\n",
+    "\n",
+    "\n",
+    "output_message = llm.invoke(\n",
+    "    [\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            [\n",
+    "                {\"type\": \"text\", \"text\": \"Transcribe the following:\"},\n",
+    "                # the audio clip says \"I'm sorry, but I can't create...\"\n",
+    "                {\n",
+    "                    \"type\": \"input_audio\",\n",
+    "                    \"input_audio\": {\"data\": audio_b64, \"format\": \"wav\"},\n",
+    "                },\n",
+    "            ],\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "output_message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feb4a499",
+   "metadata": {},
+   "source": [
+    "## Audio Generation (Preview)\n",
+    "\n",
+    ":::info\n",
+    "Requires `langchain-openai>=0.2.3`\n",
+    ":::\n",
+    "\n",
+    "OpenAI has a new [audio generation feature](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-out) that allows you to use audio inputs and outputs with the `gpt-4o-audio-preview` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f67a2cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-4o-audio-preview\",\n",
+    "    temperature=0,\n",
+    "    model_kwargs={\n",
+    "        \"modalities\": [\"text\", \"audio\"],\n",
+    "        \"audio\": {\"voice\": \"alloy\", \"format\": \"wav\"},\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "output_message = llm.invoke(\n",
+    "    [\n",
+    "        (\"human\", \"Are you made by OpenAI? Just answer yes or no\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7dd4e8b",
+   "metadata": {},
+   "source": [
+    "`output_message.additional_kwargs['audio']` will contain a dictionary like\n",
+    "```python\n",
+    "{\n",
+    "    'data': '<audio data b64-encoded',\n",
+    "    'expires_at': 1729268602,\n",
+    "    'id': 'audio_67127d6a44348190af62c1530ef0955a',\n",
+    "    'transcript': 'Yes.'\n",
+    "}\n",
+    "```\n",
+    "and the format will be what was passed in `model_kwargs['audio']['format']`.\n",
+    "\n",
+    "We can also pass this message with audio data back to the model as part of a message history before openai `expires_at` is reached.\n",
+    "\n",
+    ":::note\n",
+    "Output audio is stored under the `audio` key in `AIMessage.additional_kwargs`, but input content blocks are typed with an `input_audio` type and key in `HumanMessage.content` lists. \n",
+    "\n",
+    "For more information, see OpenAI's [audio docs](https://platform.openai.com/docs/guides/audio).\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f5ae473d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history = [\n",
+    "    (\"human\", \"Are you made by OpenAI? Just answer yes or no\"),\n",
+    "    output_message,\n",
+    "    (\"human\", \"And what is your name? Just give your name.\"),\n",
+    "]\n",
+    "second_output_message = llm.invoke(history)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a796d728-971b-408b-88d5-440015bbb941",
@@ -447,7 +601,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/chat/sambastudio.ipynb

@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: SambaStudio\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ChatSambaStudio\n",
+    "\n",
+    "This will help you getting started with SambaStudio [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
+    "\n",
+    "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatSambaStudio](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_community` integration package, and install the `SSEClient` Package.\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-community\n",
+    "pip install sseclient-py\n",
+    "```\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
+    "\n",
+    "``` bash\n",
+    "export SAMBASTUDIO_URL=\"your-api-key-here\"\n",
+    "export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"SAMBASTUDIO_URL\"):\n",
+    "    os.environ[\"SAMBASTUDIO_URL\"] = getpass.getpass(\"Enter your SambaStudio URL: \")\n",
+    "if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your SambaStudio API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain __SambaStudio__ integration lives in the `langchain_community` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-community\n",
+    "%pip install -qu sseclient-py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.sambanova import ChatSambaStudio\n",
+    "\n",
+    "llm = ChatSambaStudio(\n",
+    "    model=\"Meta-Llama-3-70B-Instruct-4096\",  # set if using a CoE endpoint\n",
+    "    max_tokens=1024,\n",
+    "    temperature=0.7,\n",
+    "    top_k=1,\n",
+    "    top_p=0.01,\n",
+    "    do_sample=True,\n",
+    "    process_prompt=\"True\",  # set if using a CoE endpoint\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': \"J'adore la programmation.\", 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>system<|end_header_id|>\\n\\nYou are a helpful assistant that translates English to French. Translate the user sentence.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nI love programming.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['J', \"'\", 'ad', 'ore', ' la', ' programm', 'ation', '.'], 'total_tokens_count': 43}, 'params': {}, 'status': None}, id='item0')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': 'Ich liebe das Programmieren.', 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>system<|end_header_id|>\\n\\nYou are a helpful assistant that translates English to German.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nI love programming.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['Ich', ' liebe', ' das', ' Programm', 'ieren', '.'], 'total_tokens_count': 36}, 'params': {}, 'status': None}, id='item0')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arrr, ye landlubber! Ye be wantin' to learn about owls, eh? Well, matey, settle yerself down with a pint o' grog and listen close, for I be tellin' ye about these fascinatin' creatures o' the night!\n",
+      "\n",
+      "Owls be birds, but not just any birds, me hearty! They be nocturnal, meanin' they do their huntin' at night, when the rest o' the world be sleepin'. And they be experts at it, too! Their big, round eyes be designed for seein' in the dark, with a special reflective layer called the tapetum lucidum that helps 'em spot prey in the shadows. It's like havin' a built-in lantern, savvy?\n",
+      "\n",
+      "But that be not all, me matey! Owls also have acute hearin', which helps 'em pinpoint the slightest sounds in the dark. And their ears be asymmetrical, meanin' one ear be higher than the other, which gives 'em better depth perception. It's like havin' a built-in sonar system, arrr!\n",
+      "\n",
+      "Now, ye might be wonderin' how owls fly so silently, like ghosts in the night. Well, it be because o' their special feathers, me hearty! They have soft, fringed feathers on their wings that help reduce noise and turbulence, makin' 'em the sneakiest flyers on the seven seas... er, skies!\n",
+      "\n",
+      "Owls come in all shapes and sizes, from the tiny elf owl to the great grey owl, which be one o' the largest owl species in the world. And they be found on every continent, except Antarctica, o' course. They be solitary creatures, but some species be known to form long-term monogamous relationships, like the barn owl and its mate.\n",
+      "\n",
+      "So, there ye have it, me hearty! Owls be amazin' creatures, with their clever adaptations and stealthy ways. Now, go forth and spread the word about these magnificent birds o' the night! And remember, if ye ever encounter an owl in the wild, be sure to show respect and keep a weather eye open, or ye might just find yerself on the receivin' end o' a silent, flyin' tackle! Arrr!"
+     ]
+    }
+   ],
+   "source": [
+    "system = \"You are a helpful assistant with pirate accent.\"\n",
+    "human = \"I want to learn more about this animal: {animal}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "\n",
+    "for chunk in chain.stream({\"animal\": \"owl\"}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Async"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='The capital of France is Paris.', response_metadata={'id': 'item0', 'partial': False, 'value': {'completion': 'The capital of France is Paris.', 'logprobs': {'text_offset': [], 'top_logprobs': []}, 'prompt': '<|start_header_id|>user<|end_header_id|>\\n\\nwhat is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n', 'stop_reason': 'end_of_text', 'tokens': ['The', ' capital', ' of', ' France', ' is', ' Paris', '.'], 'total_tokens_count': 24}, 'params': {}, 'status': None}, id='item0')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"what is the capital of {country}?\",\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "await chain.ainvoke({\"country\": \"France\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Async Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantum computers use quantum bits (qubits) to process multiple possibilities simultaneously, exponentially faster than classical computers, enabling breakthroughs in fields like cryptography, optimization, and simulation."
+     ]
+    }
+   ],
+   "source": [
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"in less than {num_words} words explain me {topic} \",\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | llm\n",
+    "\n",
+    "async for chunk in chain.astream({\"num_words\": 30, \"topic\": \"quantum computers\"}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatSambaStudio features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html"
+   ]
+  }
+ ],
+ "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/airbyte.ipynb

@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "% pip install -qU langchain-airbyte"
+    "%pip install -qU langchain-airbyte"
    ]
   },
   {

docs/docs/integrations/document_loaders/athena.ipynb

@@ -24,7 +24,7 @@
    "source": [
     "## Setting up\n",
     "\n",
-    "Follow [instructions to set up an AWS accoung](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
+    "Follow [instructions to set up an AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
     "\n",
     "Install a python library:"
    ]

docs/docs/integrations/document_loaders/browserbase.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "% pip install browserbase"
+    "%pip install browserbase"
    ]
   },
   {

docs/docs/integrations/document_loaders/firecrawl.ipynb

@@ -26,33 +26,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup\n",
-    "\n",
-    "### Credentials \n",
-    "\n",
-    "You will need to get your own API key. Go to  [this page](https://firecrawl.dev) to learn more."
+    "## Setup"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"FIRECRAWL_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"FIRECRAWL_API_KEY\"] = getpass.getpass(\"Enter your Firecrawl API key: \")"
+    "pip install firecrawl-py"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Installation\n",
-    "\n",
-    "You will need to install both the `langchain_community` and `firecrawl-py` pacakges:"
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will need to get your own API key. See https://firecrawl.dev"
    ]
   },
   {
@@ -61,42 +60,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU firecrawl-py==0.0.20 langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "### Modes\n",
-    "\n",
-    "- `scrape`: Scrape single url and return the markdown.\n",
-    "- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one."
+    "from langchain_community.document_loaders.firecrawl import FireCrawlLoader"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import FireCrawlLoader\n",
-    "\n",
-    "loader = FireCrawlLoader(url=\"https://firecrawl.dev\", mode=\"crawl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -111,40 +80,14 @@
     }
    ],
    "source": [
-    "docs = loader.load()\n",
-    "\n",
-    "docs[0]"
+    "loader = FireCrawlLoader(\n",
+    "    api_key=\"YOUR_API_KEY\", url=\"https://firecrawl.dev\", mode=\"scrape\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load\n",
-    "\n",
-    "You can use lazy loading to minimize memory requirements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,39 +103,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "len(pages)"
+    "pages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modes\n",
+    "\n",
+    "- `scrape`: Scrape single url and return the markdown.\n",
+    "- `crawl`: Crawl the url and all accessible sub pages and return the markdown for each one.\n",
+    "- `map`: Maps the URL and returns a list of semantically related pages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Crawl\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\n",
-      " Join the waitlist to turn any web\n",
-      "{'ogUrl': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'title': 'Introducing Fire Engine for Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/images/blog/fire-engine-launch.png', 'ogTitle': 'Introducing Fire Engine for Firecrawl', 'sitemap': {'lastmod': '2024-08-06T00:00:00.000Z', 'changefreq': 'weekly'}, 'keywords': 'firecrawl,fireengine,web crawling,dashboard,web scraping,LLM,data extraction', 'sourceURL': 'https://www.firecrawl.dev/blog/introducing-fire-engine-for-firecrawl', 'ogSiteName': 'Firecrawl', 'description': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'ogDescription': 'The most scalable, reliable, and fast way to get web data for Firecrawl.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "loader = FireCrawlLoader(\n",
+    "    api_key=\"YOUR_API_KEY\",\n",
+    "    url=\"https://firecrawl.dev\",\n",
+    "    mode=\"crawl\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(pages[0].page_content[:100])\n",
     "print(pages[0].metadata)"
@@ -202,10 +167,54 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Crawler Options\n",
+    "#### Crawl Options\n",
     "\n",
-    "You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information.\n",
-    "\n"
+    "You can also pass `params` to the loader. This is a dictionary of options to pass to the crawler. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = FireCrawlLoader(api_key=\"YOUR_API_KEY\", url=\"firecrawl.dev\", mode=\"map\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Map Options\n",
+    "\n",
+    "You can also pass `params` to the loader. This is a dictionary of options to pass to the loader. See the [FireCrawl API documentation](https://github.com/mendableai/firecrawl-py) for more information."
    ]
   },
   {
@@ -220,7 +229,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/document_loaders/glue_catalog.ipynb

@@ -22,7 +22,7 @@
    "source": [
     "## Setting up\n",
     "\n",
-    "- Follow [instructions to set up an AWS accoung](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
+    "- Follow [instructions to set up an AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
     "- Install the boto3 library: `pip install boto3`\n"
    ]
   },

docs/docs/integrations/document_loaders/upstage.ipynb

@@ -25,9 +25,9 @@
     }
    },
    "source": [
-    "# UpstageLayoutAnalysisLoader\n",
+    "# UpstageDocumentParseLoader\n",
     "\n",
-    "This notebook covers how to get started with `UpstageLayoutAnalysisLoader`.\n",
+    "This notebook covers how to get started with `UpstageDocumentParseLoader`.\n",
     "\n",
     "## Installation\n",
     "\n",
@@ -89,10 +89,10 @@
     }
    ],
    "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "from langchain_upstage import UpstageDocumentParseLoader\n",
     "\n",
     "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "layzer = UpstageDocumentParseLoader(file_path, split=\"page\")\n",
     "\n",
     "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
     "docs = layzer.load()  # or layzer.lazy_load()\n",

docs/docs/integrations/graphs/azure_cosmosdb_gremlin.ipynb

@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "graph = GremlinGraph(\n",
-    "    url=f\"=wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/\",\n",
+    "    url=f\"wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/\",\n",
     "    username=f\"/dbs/{cosmosdb_db_id}/colls/{cosmosdb_db_graph_id}\",\n",
     "    password=cosmosdb_access_Key,\n",
     ")"

docs/docs/integrations/llms/friendli.ipynb

@@ -29,7 +29,7 @@
     "Ensure the `langchain_community` and `friendli-client` are installed.\n",
     "\n",
     "```sh\n",
-    "pip install -U langchain-comminity friendli-client.\n",
+    "pip install -U langchain-community friendli-client.\n",
     "```\n",
     "\n",
     "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."

docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb

@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NVIDIA\n",
+    "\n",
+    "This will help you getting started with NVIDIA [models](/docs/concepts/#llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
+    "\n",
+    "## Overview\n",
+    "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
+    "NVIDIA NIM inference microservice. These models are optimized by NVIDIA to deliver the best performance on NVIDIA \n",
+    "accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single \n",
+    "command on NVIDIA accelerated infrastructure.\n",
+    "\n",
+    "NVIDIA hosted deployments of NIMs are available to test on the [NVIDIA API catalog](https://build.nvidia.com/). After testing, \n",
+    "NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, \n",
+    "giving enterprises ownership and full control of their IP and AI application.\n",
+    "\n",
+    "NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. \n",
+    "At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with NVIDIA supported via the `NVIDIA` class.\n",
+    "\n",
+    "For more information on accessing the llm models through this api, check out the [NVIDIA](https://python.langchain.com/docs/integrations/llms/nvidia_ai_endpoints/) documentation.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [NVIDIA](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html) | [langchain_nvidia_ai_endpoints](https://python.langchain.com/api_reference/nvidia_ai_endpoints/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "**To get started:**\n",
+    "\n",
+    "1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.\n",
+    "\n",
+    "2. Click on your model of choice.\n",
+    "\n",
+    "3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
+    "\n",
+    "4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints.\n",
+    "\n",
+    "### Credentials\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NVIDIA_API_KEY\"):\n",
+    "    # Note: the API key should start with \"nvapi-\"\n",
+    "    os.environ[\"NVIDIA_API_KEY\"] = getpass.getpass(\"Enter your NVIDIA API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain NVIDIA AI Endpoints integration lives in the `langchain_nvidia_ai_endpoints` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "See [LLM](/docs/how_to#llms) for full functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_nvidia_ai_endpoints import NVIDIA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = NVIDIA().bind(max_tokens=256)\n",
+    "llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = \"# Function that does quicksort written in Rust without comments:\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(llm.invoke(prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stream, Batch, and Async\n",
+    "\n",
+    "These models natively support streaming, and as is the case with all LangChain LLMs they expose a batch method to handle concurrent requests, as well as async methods for invoke, stream, and batch. Below are a few examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for chunk in llm.stream(prompt):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm.batch([prompt])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await llm.ainvoke(prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async for chunk in llm.astream(prompt):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await llm.abatch([prompt])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async for chunk in llm.astream_log(prompt):\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = llm.invoke(\n",
+    "    \"X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1) #Train a logistic regression model, predict the labels on the test set and compute the accuracy score\"\n",
+    ")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Supported models\n",
+    "\n",
+    "Querying `available_models` will still give you all of the other models offered by your API credentials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NVIDIA.get_available_models()\n",
+    "# llm.get_available_models()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `NVIDIA` features and configurations head to the API reference: https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.llms.NVIDIA.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-nvidia-ai-endpoints-m0-Y4aGr-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/llms/oci_model_deployment_endpoint.ipynb

@@ -8,9 +8,11 @@
     "\n",
     "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure.\n",
     "\n",
+    "> For the latest updates, examples and experimental features, please see [ADS LangChain Integration](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/large_language_model/langchain_models.html).\n",
+    "\n",
     "This notebooks goes over how to use an LLM hosted on a [OCI Data Science Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm).\n",
     "\n",
-    "To authenticate, [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) has been used to automatically load credentials for invoking endpoint."
+    "For authentication, the [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) library is used to automatically load credentials required for invoking the endpoint."
    ]
   },
   {
@@ -29,29 +31,52 @@
     "## Prerequisite\n",
     "\n",
     "### Deploy model\n",
-    "Check [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/tree/main/model-deployment/containers/llama2) on how to deploy your llm on OCI Data Science Model deployment.\n",
+    "You can easily deploy, fine-tune, and evaluate foundation models using the [AI Quick Actions](https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions.htm) on OCI Data Science Model deployment. For additional deployment examples, please visit the [Oracle GitHub samples repository](https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-quick-actions/llama3-with-smc.md). \n",
     "\n",
     "### Policies\n",
     "Make sure to have the required [policies](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm#model_dep_policies_auth__predict-endpoint) to access the OCI Data Science Model Deployment endpoint.\n",
     "\n",
     "## Set up\n",
     "\n",
-    "### vLLM\n",
-    "After having deployed model, you have to set up following required parameters of the `OCIModelDeploymentVLLM` call:\n",
+    "After having deployed model, you have to set up following required parameters of the call:\n",
     "\n",
-    "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https://<MD_OCID>/predict`. \n",
-    "- **`model`**: The location of the model.\n",
+    "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict`. \n",
     "\n",
-    "### Text generation inference (TGI)\n",
-    "You have to set up following required parameters of the `OCIModelDeploymentTGI` call:\n",
-    "\n",
-    "- **`endpoint`**: The model HTTP endpoint from the deployed model, e.g. `https://<MD_OCID>/predict`. \n",
     "\n",
     "### Authentication\n",
     "\n",
     "You can set authentication through either ads or environment variables. When you are working in OCI Data Science Notebook Session, you can leverage resource principal to access other OCI resources. Check out [here](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html) to see more options. \n",
     "\n",
-    "## Example"
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ads\n",
+    "from langchain_community.llms import OCIModelDeploymentLLM\n",
+    "\n",
+    "# Set authentication through ads\n",
+    "# Use resource principal are operating within a\n",
+    "# OCI service that has resource principal based\n",
+    "# authentication configured\n",
+    "ads.set_auth(\"resource_principal\")\n",
+    "\n",
+    "# Create an instance of OCI Model Deployment Endpoint\n",
+    "# Replace the endpoint uri and model name with your own\n",
+    "# Using generic class as entry point, you will be able\n",
+    "# to pass model parameters through model_kwargs during\n",
+    "# instantiation.\n",
+    "llm = OCIModelDeploymentLLM(\n",
+    "    endpoint=\"https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict\",\n",
+    "    model=\"odsc-llm\",\n",
+    ")\n",
+    "\n",
+    "# Run the LLM\n",
+    "llm.invoke(\"Who is the first president of United States?\")"
    ]
   },
   {
@@ -71,7 +96,11 @@
     "\n",
     "# Create an instance of OCI Model Deployment Endpoint\n",
     "# Replace the endpoint uri and model name with your own\n",
-    "llm = OCIModelDeploymentVLLM(endpoint=\"https://<MD_OCID>/predict\", model=\"model_name\")\n",
+    "# Using framework specific class as entry point, you will\n",
+    "# be able to pass model parameters in constructor.\n",
+    "llm = OCIModelDeploymentVLLM(\n",
+    "    endpoint=\"https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict\",\n",
+    ")\n",
     "\n",
     "# Run the LLM\n",
     "llm.invoke(\"Who is the first president of United States?\")"
@@ -97,14 +126,64 @@
     "\n",
     "# Set endpoint through environment variables\n",
     "# Replace the endpoint uri with your own\n",
-    "os.environ[\"OCI_LLM_ENDPOINT\"] = \"https://<MD_OCID>/predict\"\n",
+    "os.environ[\"OCI_LLM_ENDPOINT\"] = (\n",
+    "    \"https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict\"\n",
+    ")\n",
     "\n",
     "# Create an instance of OCI Model Deployment Endpoint\n",
+    "# Using framework specific class as entry point, you will\n",
+    "# be able to pass model parameters in constructor.\n",
     "llm = OCIModelDeploymentTGI()\n",
     "\n",
     "# Run the LLM\n",
     "llm.invoke(\"Who is the first president of United States?\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Asynchronous calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await llm.ainvoke(\"Tell me a joke.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming calls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for chunk in llm.stream(\"Tell me a joke.\"):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For comprehensive details on all features and configurations, please refer to the API reference documentation for each class:\n",
+    "\n",
+    "* [OCIModelDeploymentLLM](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentLLM.html)\n",
+    "* [OCIModelDeploymentVLLM](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentVLLM.html)\n",
+    "* [OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html)"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/providers/baai.mdx

@@ -0,0 +1,44 @@
+# BAAI
+
+>[Beijing Academy of Artificial Intelligence (BAAI) (Wikipedia)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence), 
+> also known as `Zhiyuan Institute`, is a Chinese non-profit artificial 
+> intelligence (AI) research laboratory. `BAAI` conducts AI research 
+> and is dedicated to promoting collaboration among academia and industry, 
+> as well as fostering top talent and a focus on long-term research on 
+> the fundamentals of AI technology. As a collaborative hub, BAAI's founding 
+> members include leading AI companies, universities, and research institutes.
+
+
+## Embedding Models
+
+### HuggingFaceBgeEmbeddings
+
+>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) 
+> are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
+
+See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
+
+```python
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+```
+
+### IpexLLMBgeEmbeddings
+
+>[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch 
+> library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, 
+> discrete GPU such as Arc, Flex and Max) with very low latency.
+
+See a [usage example running model on Intel CPU](/docs/integrations/text_embedding/ipex_llm).
+See a [usage example running model on Intel GPU](/docs/integrations/text_embedding/ipex_llm_gpu).
+
+```python
+from langchain_community.embeddings import IpexLLMBgeEmbeddings
+```
+
+### QuantizedBgeEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/itrex).
+
+```python
+from langchain_community.embeddings import QuantizedBgeEmbeddings
+```

docs/docs/integrations/providers/byte_dance.mdx

@@ -8,15 +8,23 @@ Get the access token.
 You can find the access instructions [here](https://open.larksuite.com/document)
 
 
-## Document Loader
-
-### Lark Suite
+## Document Loaders
 
 >[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform 
 > developed by `ByteDance`.
 
-See a [usage example](/docs/integrations/document_loaders/larksuite).
+### Lark Suite for Document
+
+See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-document).
 
 ```python
 from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
 ```
+
+### Lark Suite for Wiki
+
+See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-wiki).
+
+```python
+from langchain_community.document_loaders.larksuite import LarkSuiteWikiLoader
+```

docs/docs/integrations/providers/databricks.md

@@ -113,7 +113,7 @@ See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/
 Open Models
 -----------
 
-To directly integrate Databricks's open models hosted on HuggingFace, you can use the [HuggingFace Integration](/docs/integrations/platforms/huggingface) of LangChain.
+To directly integrate Databricks's open models hosted on HuggingFace, you can use the [HuggingFace Integration](/docs/integrations/providers/huggingface) of LangChain.
 
 ```
 from langchain_huggingface import HuggingFaceEndpoint

docs/docs/integrations/providers/google.mdx

@@ -72,7 +72,7 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
 from langchain_google_vertexai import ChatVertexAI
 ```
 
-### Chat Anthropic on Vertex AI Model Garden
+### Anthropic on Vertex AI Model Garden
 
 See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
 
@@ -80,19 +80,19 @@ See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
 from langchain_google_vertexai.model_garden import ChatAnthropicVertex
 ```
 
-### Chat Llama on Vertex AI Model Garden
+### Llama on Vertex AI Model Garden
 
 ```python
 from langchain_google_vertexai.model_garden_maas.llama import VertexModelGardenLlama
 ```
 
-### Chat Mistral on Vertex AI Model Garden
+### Mistral on Vertex AI Model Garden
 
 ```python
 from langchain_google_vertexai.model_garden_maas.mistral import VertexModelGardenMistral
 ```
 
-### Chat Gemma local from Hugging Face
+### Gemma local from Hugging Face
 
 >Local `Gemma` model loaded from `HuggingFace`.
 
@@ -106,7 +106,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatLocalHF
 ```
 
-### Chat Gemma local from Kaggle
+### Gemma local from Kaggle
 
 >Local `Gemma` model loaded from `Kaggle`.
 
@@ -120,7 +120,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatLocalKaggle
 ```
 
-### Chat Gemma on Vertex AI Model Garden
+### Gemma on Vertex AI Model Garden
 
 We need to install `langchain-google-vertexai` python package.
 
@@ -132,7 +132,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatVertexAIModelGarden
 ```
 
-### Vertex AI image captioning chat
+### Vertex AI image captioning
 
 >Implementation of the `Image Captioning model` as a chat.
 
@@ -146,7 +146,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageCaptioningChat
 ```
 
-### Vertex AI image editor chat
+### Vertex AI image editor
 
 >Given an image and a prompt, edit the image. Currently only supports mask-free editing.
 
@@ -160,7 +160,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageEditorChat
 ```
 
-### Vertex AI image generator chat
+### Vertex AI image generator
 
 >Generates an image from a prompt.
 
@@ -174,7 +174,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageGeneratorChat
 ```
 
-### Vertex AI visual QnA chat
+### Vertex AI visual QnA
 
 >Chat implementation of a visual QnA model
 

docs/docs/integrations/providers/jina.mdx

@@ -1,20 +1,35 @@
-# Jina
+# Jina AI
 
-This page covers how to use the Jina Embeddings within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
+>[Jina AI](https://jina.ai/about-us) is a search AI company. `Jina` helps businesses and developers unlock multimodal data with a better search.
 
 ## Installation and Setup
 - Get a Jina AI API token from [here](https://jina.ai/embeddings/) and set it as an environment variable (`JINA_API_TOKEN`)
 
-There exists a Jina Embeddings wrapper, which you can access with 
+## Chat Models
 
 ```python
-from langchain_community.embeddings import JinaEmbeddings
-
-# you can pas jina_api_key, if none is passed it will be taken from `JINA_API_TOKEN` environment variable
-embeddings = JinaEmbeddings(jina_api_key='jina_**', model_name='jina-embeddings-v2-base-en')
+from langchain_community.chat_models import JinaChat
 ```
 
+See a [usage examples](/docs/integrations/chat/jinachat).
+
+## Embedding Models
+
 You can check the list of available models from [here](https://jina.ai/embeddings/)
 
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina)
+```python
+from langchain_community.embeddings import JinaEmbeddings
+```
+
+See a [usage examples](/docs/integrations/text_embedding/jina).
+
+## Document Transformers
+
+### Jina Rerank
+
+```python
+from langchain_community.document_compressors import JinaRerank
+```
+
+See a [usage examples](/docs/integrations/document_transformers/jina_rerank).
+

docs/docs/integrations/providers/koboldai.mdx

@@ -0,0 +1,20 @@
+# KoboldAI
+
+>[KoboldAI](https://koboldai.com/) is a free, open-source project that allows users to run AI models locally 
+> on their own computer. 
+> It's a browser-based front-end that can be used for writing or role playing with an AI.
+>[KoboldAI](https://github.com/KoboldAI/KoboldAI-Client) is a "a browser-based front-end for 
+> AI-assisted writing with multiple local & remote AI models...". 
+> It has a public and local API that can be used in LangChain.
+
+## Installation and Setup
+
+Check out the [installation guide](https://github.com/KoboldAI/KoboldAI-Client).
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/koboldai).
+
+```python
+from langchain_community.llms import KoboldApiLLM
+```

docs/docs/integrations/providers/konlpy.mdx

@@ -0,0 +1,21 @@
+# KoNLPY
+
+>[KoNLPy](https://konlpy.org/) is a Python package for natural language processing (NLP) 
+> of the Korean language.
+
+
+## Installation and Setup
+
+You need to install the `konlpy` python package.
+
+```bash
+pip install konlpy
+```
+
+## Text splitter
+
+See a [usage example](/docs/how_to/split_by_token/#konlpy).
+
+```python
+from langchain_text_splitters import KonlpyTextSplitter
+```

docs/docs/integrations/providers/kuzu.mdx

@@ -0,0 +1,32 @@
+# Kùzu
+
+>[Kùzu](https://kuzudb.com/) is a company based in Waterloo, Ontario, Canada. 
+> It provides a highly scalable, extremely fast, easy-to-use [embeddable graph database](https://github.com/kuzudb/kuzu).
+
+
+
+## Installation and Setup
+
+You need to install the `kuzu` python package.
+
+```bash
+pip install kuzu
+```
+
+## Graph database
+
+See a [usage example](/docs/integrations/graphs/kuzu_db).
+
+```python
+from langchain_community.graphs import KuzuGraph
+```
+
+## Chain
+
+See a [usage example](/docs/integrations/graphs/kuzu_db/#creating-kuzuqachain).
+
+```python
+from langchain.chains import KuzuQAChain
+```
+
+

docs/docs/integrations/providers/llama_index.mdx

@@ -0,0 +1,32 @@
+# LlamaIndex
+
+>[LlamaIndex](https://www.llamaindex.ai/) is the leading data framework for building LLM applications
+
+
+## Installation and Setup
+
+You need to install the `llama-index` python package.
+
+```bash
+pip install llama-index
+```
+
+See the [installation instructions](https://docs.llamaindex.ai/en/stable/getting_started/installation/).
+
+## Retrievers
+
+### LlamaIndexRetriever
+
+>It is used for the question-answering with sources over an LlamaIndex data structure.
+
+```python
+from langchain_community.retrievers.llama_index import LlamaIndexRetriever
+```
+
+### LlamaIndexGraphRetriever
+
+>It is used for question-answering with sources over an LlamaIndex graph data structure.
+
+```python
+from langchain_community.retrievers.llama_index import LlamaIndexGraphRetriever
+```

docs/docs/integrations/providers/llamaedge.mdx

@@ -0,0 +1,24 @@
+# LlamaEdge
+
+>[LlamaEdge](https://llamaedge.com/docs/intro/) is the easiest & fastest way to run customized 
+> and fine-tuned LLMs locally or on the edge.
+>
+>* Lightweight inference apps. `LlamaEdge` is in MBs instead of GBs
+>* Native and GPU accelerated performance
+>* Supports many GPU and hardware accelerators
+>* Supports many optimized inference libraries
+>* Wide selection of AI / LLM models
+
+
+
+## Installation and Setup
+
+See the [installation instructions](https://llamaedge.com/docs/user-guide/quick-start-command).
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/llama_edge).
+
+```python
+from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
+```

docs/docs/integrations/providers/llamafile.mdx

@@ -0,0 +1,31 @@
+# llamafile
+
+>[llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs 
+> with a single file.
+
+>`llamafile` makes open LLMs much more accessible to both developers and end users. 
+> `llamafile` is doing that by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with 
+> [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses 
+> all the complexity of LLMs down to a single-file executable (called a "llamafile") 
+> that runs locally on most computers, with no installation.
+
+
+## Installation and Setup
+
+See the [installation instructions](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#quickstart).
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/llamafile).
+
+```python
+from langchain_community.llms.llamafile import Llamafile
+```
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/llamafile).
+
+```python
+from langchain_community.embeddings import LlamafileEmbeddings
+```

docs/docs/integrations/providers/localai.mdx

@@ -0,0 +1,24 @@
+# LocalAI
+
+>[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative. 
+> `LocalAI` act as a drop-in replacement REST API that’s compatible with OpenAI API 
+> specifications for local inferencing. It allows you to run LLMs, generate images, 
+> audio (and not only) locally or on-prem with consumer grade hardware, 
+> supporting multiple model families and architectures.
+
+## Installation and Setup
+
+We have to install several python packages: 
+
+```bash
+pip install tenacity openai
+```
+
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/localai).
+
+```python
+from langchain_community.embeddings import LocalAIEmbeddings
+```

docs/docs/integrations/providers/microsoft.mdx

@@ -264,22 +264,20 @@ See a [usage example](/docs/integrations/document_loaders/url/#playwright-url-lo
 from langchain_community.document_loaders.onenote import OneNoteLoader
 ```
 
-## AI Agent Memory System
-
-[AI agent](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents) needs robust memory systems that support multi-modality, offer strong operational performance, and enable agent memory sharing as well as separation.
+## Vector Stores
 
 ### Azure Cosmos DB
 AI agents can rely on Azure Cosmos DB as a unified [memory system](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents#memory-can-make-or-break-agents) solution, enjoying speed, scale, and simplicity. This service successfully [enabled OpenAI's ChatGPT service](https://www.youtube.com/watch?v=6IIUtEFKJec&t) to scale dynamically with high reliability and low maintenance. Powered by an atom-record-sequence engine, it is the world's first globally distributed [NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-nosql), [relational](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-relational), and [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) service that offers a serverless mode. 
 
 Below are two available Azure Cosmos DB APIs that can provide vector store functionalities.
 
-### Azure Cosmos DB for MongoDB (vCore)
+#### Azure Cosmos DB for MongoDB (vCore)
 
 >[Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) makes it easy to create a database with full native MongoDB support.
 > You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account's connection string.
 > Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based applications with your data that's stored in Azure Cosmos DB.
 
-#### Installation and Setup
+##### Installation and Setup
 
 See [detail configuration instructions](/docs/integrations/vectorstores/azure_cosmos_db).
 
@@ -289,7 +287,7 @@ We need to install `pymongo` python package.
 pip install pymongo
 ```
 
-#### Deploy Azure Cosmos DB on Microsoft Azure
+##### Deploy Azure Cosmos DB on Microsoft Azure
 
 Azure Cosmos DB for MongoDB vCore provides developers with a fully managed MongoDB-compatible database service for building modern applications with a familiar architecture.
 
@@ -303,7 +301,7 @@ See a [usage example](/docs/integrations/vectorstores/azure_cosmos_db).
 from langchain_community.vectorstores import AzureCosmosDBVectorSearch
 ```
 
-### Azure Cosmos DB NoSQL
+#### Azure Cosmos DB NoSQL
 
 >[Azure Cosmos DB for NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search) now offers vector indexing and search in preview.
 This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors
@@ -312,7 +310,7 @@ but also high-dimensional vectors as other properties of the documents. This col
 as the vectors are stored in the same logical unit as the data they represent. This simplifies data management, AI application architectures, and the
 efficiency of vector-based operations.
 
-#### Installation and Setup
+##### Installation and Setup
 
 See [detail configuration instructions](/docs/integrations/vectorstores/azure_cosmos_db_no_sql).
 
@@ -322,7 +320,7 @@ We need to install `azure-cosmos` python package.
 pip install azure-cosmos
 ```
 
-#### Deploy Azure Cosmos DB on Microsoft Azure
+##### Deploy Azure Cosmos DB on Microsoft Azure
 
 Azure Cosmos DB offers a solution for modern apps and intelligent workloads by being very responsive with dynamic and elastic autoscale. It is available
 in every Azure region and can automatically replicate data closer to users. It has SLA guaranteed low-latency and high availability.
@@ -336,6 +334,7 @@ from langchain_community.vectorstores import AzureCosmosDBNoSQLVectorSearch
 ```
 
 ### Azure Database for PostgreSQL
+
 >[Azure Database for PostgreSQL - Flexible Server](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview) is a relational database service based on the open-source Postgres database engine. It's a fully managed database-as-a-service that can handle mission-critical workloads with predictable performance, security, high availability, and dynamic scalability.
 
 See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. 
@@ -446,6 +445,38 @@ The `azure_ai_services` toolkit includes the following tools:
 - Text to Speech: [AzureAiServicesTextToSpeechTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_to_speech.AzureAiServicesTextToSpeechTool.html)
 - Text Analytics for Health: [AzureAiServicesTextAnalyticsForHealthTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_analytics_for_health.AzureAiServicesTextAnalyticsForHealthTool.html)
 
+### Azure Cognitive Services
+
+We need to install several python packages.
+
+```bash
+pip install azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis
+```
+
+See a [usage example](/docs/integrations/tools/azure_cognitive_services).
+
+```python
+from langchain_community.agent_toolkits import AzureCognitiveServicesToolkit
+```
+
+#### Azure AI Services individual tools
+
+The `azure_ai_services` toolkit includes the tools that queries the `Azure Cognitive Services`:
+- `AzureCogsFormRecognizerTool`: Form Recognizer API
+- `AzureCogsImageAnalysisTool`: Image Analysis API
+- `AzureCogsSpeech2TextTool`: Speech2Text API
+- `AzureCogsText2SpeechTool`: Text2Speech API
+- `AzureCogsTextAnalyticsHealthTool`: Text Analytics for Health API
+
+```python
+from langchain_community.tools.azure_cognitive_services import (
+    AzureCogsFormRecognizerTool,
+    AzureCogsImageAnalysisTool,
+    AzureCogsSpeech2TextTool,
+    AzureCogsText2SpeechTool,
+    AzureCogsTextAnalyticsHealthTool,
+)
+```
 
 ### Microsoft Office 365 email and calendar
 
@@ -465,11 +496,11 @@ from langchain_community.agent_toolkits import O365Toolkit
 #### Office 365 individual tools
 
 You can use individual tools from the Office 365 Toolkit:
-- `O365CreateDraftMessage`: tool for creating a draft email in Office 365
-- `O365SearchEmails`: tool for searching email messages in Office 365
-- `O365SearchEvents`: tool for searching calendar events in Office 365
-- `O365SendEvent`: tool for sending calendar events in Office 365
-- `O365SendMessage`: tool for sending an email in Office 365
+- `O365CreateDraftMessage`: creating a draft email in Office 365
+- `O365SearchEmails`: searching email messages in Office 365
+- `O365SearchEvents`: searching calendar events in Office 365
+- `O365SendEvent`: sending calendar events in Office 365
+- `O365SendMessage`: sending an email in Office 365
 
 ```python
 from langchain_community.tools.office365 import O365CreateDraftMessage
@@ -497,9 +528,9 @@ from langchain_community.utilities.powerbi import PowerBIDataset
 #### PowerBI individual tools
 
 You can use individual tools from the Azure PowerBI Toolkit:
-- `InfoPowerBITool`: tool for getting metadata about a PowerBI Dataset
-- `ListPowerBITool`: tool for getting tables names
-- `QueryPowerBITool`: tool for querying a PowerBI Dataset
+- `InfoPowerBITool`: getting metadata about a PowerBI Dataset
+- `ListPowerBITool`: getting tables names
+- `QueryPowerBITool`: querying a PowerBI Dataset
 
 ```python
 from langchain_community.tools.powerbi.tool import InfoPowerBITool

docs/docs/integrations/providers/milvus.mdx

@@ -9,17 +9,25 @@
 Install the Python SDK:
 
 ```bash
-pip install pymilvus
+pip install langchain-milvus
 ```
 
 ## Vector Store
 
-There exists a wrapper around `Milvus` indexes, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
+See a [usage example](/docs/integrations/vectorstores/milvus).
 
 To import this vectorstore:
 ```python
-from langchain_community.vectorstores import Milvus
+from langchain_milvus import Milvus
+```
+
+## Retrievers
+
+See a [usage example](/docs/integrations/retrievers/milvus_hybrid_search).
+
+To import this vectorstore:
+```python
+from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
+from langchain_milvus.utils.sparse import BM25SparseEmbedding
 ```
 
-For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus)

docs/docs/integrations/providers/mongodb.mdx

@@ -0,0 +1,24 @@
+# MongoDB
+
+>[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented 
+> database that supports JSON-like documents with a dynamic schema.
+ 
+**NOTE:** 
+- See other `MongoDB` integrations on the [MongoDB Atlas page](/docs/integrations/providers/mongodb_atlas).
+
+## Installation and Setup
+
+Install the Python package:
+
+```bash
+pip install langchain-mongodb
+```
+
+## Message Histories
+
+See a [usage example](/docs/integrations/memory/mongodb_chat_message_history).
+
+To import this vectorstore:
+```python
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+```

docs/docs/integrations/providers/mongodb_atlas.mdx

@@ -22,10 +22,30 @@ See a [usage example](/docs/integrations/vectorstores/mongodb_atlas).
 from langchain_mongodb import MongoDBAtlasVectorSearch
 ```
 
+## Retrievers
 
-## LLM Caches
+### Full Text Search Retriever
+
+>`Hybrid Search Retriever` performs full-text searches using 
+> Lucene’s standard (`BM25`) analyzer.
+
+```python
+from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever
+```
+
+### Hybrid Search Retriever
+
+>`Hybrid Search Retriever` combines vector and full-text searches weighting 
+> them the via `Reciprocal Rank Fusion` (`RRF`) algorithm.
+ 
+```python
+from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever
+```
+
+## Model Caches
 
 ### MongoDBCache
+
 An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.
 
 To import this cache:

docs/docs/integrations/providers/oci.mdx

@@ -32,20 +32,18 @@ from langchain_community.embeddings import OCIGenAIEmbeddings
 > as an OCI Model Deployment Endpoint using the
 > [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm).
 
-If you deployed a LLM with the VLLM or TGI framework, you can use the
-`OCIModelDeploymentVLLM` or `OCIModelDeploymentTGI` classes to interact with it.
-
 To use, you should have the latest `oracle-ads` python SDK installed.
 
 ```bash
 pip install -U oracle-ads
 ```
 
-See [usage examples](/docs/integrations/llms/oci_model_deployment_endpoint).
+See [chat](/docs/integrations/chat/oci_data_science) and [complete](/docs/integrations/llms/oci_model_deployment_endpoint) usage examples.
+
 
 ```python
-from langchain_community.llms import OCIModelDeploymentVLLM
+from langchain_community.chat_models import ChatOCIModelDeployment
 
-from langchain_community.llms import OCIModelDeploymentTGI
+from langchain_community.llms import OCIModelDeploymentLLM
 ```
 

docs/docs/integrations/providers/upstage.ipynb

@@ -10,7 +10,7 @@
     ">\n",
     ">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
     "\n",
-    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
+    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Document Parse** and **Groundedness Check**. \n"
    ]
   },
   {
@@ -24,7 +24,7 @@
     "| Chat | Build assistants using Solar Mini Chat | `from langchain_upstage import ChatUpstage` | [Go](../../chat/upstage) |\n",
     "| Text Embedding | Embed strings to vectors | `from langchain_upstage import UpstageEmbeddings` | [Go](../../text_embedding/upstage) |\n",
     "| Groundedness Check | Verify groundedness of assistant's response | `from langchain_upstage import UpstageGroundednessCheck` | [Go](../../tools/upstage_groundedness_check) |\n",
-    "| Layout Analysis | Serialize documents with tables and figures | `from langchain_upstage import UpstageLayoutAnalysisLoader` | [Go](../../document_loaders/upstage) |\n",
+    "| Document Parse | Serialize documents with tables and figures | `from langchain_upstage import UpstageDocumentParseLoader` | [Go](../../document_loaders/upstage) |\n",
     "\n",
     "See [documentations](https://developers.upstage.ai/) for more details about the features."
    ]
@@ -122,7 +122,7 @@
    "source": [
     "## Document loader\n",
     "\n",
-    "### Layout Analysis\n",
+    "### Document Parse\n",
     "\n",
     "See [a usage example](/docs/integrations/document_loaders/upstage)."
    ]
@@ -133,10 +133,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "from langchain_upstage import UpstageDocumentParseLoader\n",
     "\n",
     "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "layzer = UpstageDocumentParseLoader(file_path, split=\"page\")\n",
     "\n",
     "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
     "docs = layzer.load()  # or layzer.lazy_load()\n",

docs/docs/integrations/retrievers/box.ipynb

@@ -52,18 +52,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "b87a8e8b-9b5a-4e78-97e4-274b6b0dd29f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter your Box Developer Token:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -81,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
@@ -102,10 +94,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
-   "outputs": [],
+   "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-box"
    ]
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
    "metadata": {},
    "outputs": [],
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 6,
    "id": "97f3ae67",
    "metadata": {},
    "outputs": [
@@ -156,7 +156,7 @@
        "[Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "ee0e726d-9974-4aa0-9ce1-0057ec3e540a",
    "metadata": {},
    "outputs": [],
@@ -216,17 +216,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased was the **Gravitational Wave Detector Kit** from AstroTech Solutions, which cost $800.')]"
+       "[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.')]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,6 +237,80 @@
     "retriever.invoke(query)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "31a59a51",
+   "metadata": {},
+   "source": [
+    "## Citations\n",
+    "\n",
+    "With Box AI and the `BoxRetriever`, you can return the answer to your prompt, return the citations used by Box to get that answer, or both. No matter how you choose to use Box AI, the retriever returns a `List[Document]` object. We offer this flexibility with two `bool` arguments, `answer` and `citations`. Answer defaults to `True` and citations defaults to `False`, do you can omit both if you just want the answer. If you want both, you can just include `citations=True` and if you only want citations, you would include `answer=False` and `citations=True`\n",
+    "\n",
+    "### Get both"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2eddc8c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.'),\n",
+       " Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = BoxRetriever(\n",
+    "    box_developer_token=box_developer_token, box_file_ids=box_file_ids, citations=True\n",
+    ")\n",
+    "\n",
+    "retriever.invoke(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2e93a2e",
+   "metadata": {},
+   "source": [
+    "### Citations only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c1892b07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = BoxRetriever(\n",
+    "    box_developer_token=box_developer_token,\n",
+    "    box_file_ids=box_file_ids,\n",
+    "    answer=False,\n",
+    "    citations=True,\n",
+    ")\n",
+    "\n",
+    "retriever.invoke(query)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
@@ -260,7 +334,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "Enter your OpenAI key:  ········\n"

docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb

@@ -25,7 +25,7 @@
     "\n",
     "| Provider | Package |\n",
     "|:--------:|:-------:|\n",
-    "| [Google](https://python.langchain.com/docs/integrations/platforms/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
+    "| [Google](https://python.langchain.com/docs/integrations/providers/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/integrations/text_embedding/ipex_llm.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Local BGE Embeddings with IPEX-LLM on Intel CPU\n",
+    "# IPEX-LLM: Local BGE Embeddings on Intel CPU\n",
     "\n",
     "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.\n",
     "\n",
@@ -92,10 +92,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Local BGE Embeddings with IPEX-LLM on Intel GPU\n",
+    "# IPEX-LLM: Local BGE Embeddings on Intel GPU\n",
     "\n",
     "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.\n",
     "\n",
@@ -155,10 +155,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/text_embedding/jina.ipynb

@@ -5,7 +5,11 @@
    "id": "1c0cf975",
    "metadata": {},
    "source": [
-    "# Jina"
+    "# Jina\n",
+    "\n",
+    "You can check the list of available models from [here](https://jina.ai/embeddings/).\n",
+    "\n",
+    "## Installation and setup"
    ]
   },
   {
@@ -231,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/databricks.ipynb

@@ -74,6 +74,24 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,

docs/docs/integrations/tools/jira.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "The `Jira` toolkit allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.html\n",
     "\n",
+    "## Installation and setup\n",
+    "\n",
     "To use this tool, you must first set as environment variables:\n",
     "    JIRA_API_TOKEN\n",
     "    JIRA_USERNAME\n",
@@ -47,7 +49,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-community"
+    "%pip install -qU langchain-community langchain_openai"
    ]
   },
   {
@@ -58,6 +60,13 @@
     "ExecuteTime": {
      "end_time": "2023-04-17T10:21:23.730922Z",
      "start_time": "2023-04-17T10:21:22.911233Z"
+    },
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:40:07.356954Z",
+     "iopub.status.busy": "2024-10-02T17:40:07.356792Z",
+     "iopub.status.idle": "2024-10-02T17:40:07.359943Z",
+     "shell.execute_reply": "2024-10-02T17:40:07.359476Z",
+     "shell.execute_reply.started": "2024-10-02T17:40:07.356942Z"
     }
    },
    "outputs": [],
@@ -72,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "b3050b55",
    "metadata": {
     "ExecuteTime": {
@@ -80,6 +89,13 @@
      "start_time": "2023-04-17T10:22:42.499447Z"
     },
     "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:40:16.201684Z",
+     "iopub.status.busy": "2024-10-02T17:40:16.200922Z",
+     "iopub.status.idle": "2024-10-02T17:40:16.208035Z",
+     "shell.execute_reply": "2024-10-02T17:40:16.207564Z",
+     "shell.execute_reply.started": "2024-10-02T17:40:16.201634Z"
+    },
     "jupyter": {
      "outputs_hidden": false
     }
@@ -93,6 +109,74 @@
     "os.environ[\"JIRA_CLOUD\"] = \"True\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c0768000-227b-4aa1-a838-4befbdefadb1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:42:00.792867Z",
+     "iopub.status.busy": "2024-10-02T17:42:00.792365Z",
+     "iopub.status.idle": "2024-10-02T17:42:00.816979Z",
+     "shell.execute_reply": "2024-10-02T17:42:00.816419Z",
+     "shell.execute_reply.started": "2024-10-02T17:42:00.792827Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "jira = JiraAPIWrapper()\n",
+    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "961b3187-daf0-4907-9cc0-a69796fba4aa",
+   "metadata": {},
+   "source": [
+    "## Tool usage\n",
+    "\n",
+    "Let's see what individual tools are in the Jira toolkit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eb5cf521-9a91-44bc-b68e-bc4067d05a76",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:42:27.232022Z",
+     "iopub.status.busy": "2024-10-02T17:42:27.231140Z",
+     "iopub.status.idle": "2024-10-02T17:42:27.240169Z",
+     "shell.execute_reply": "2024-10-02T17:42:27.239693Z",
+     "shell.execute_reply.started": "2024-10-02T17:42:27.231949Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('JQL Query',\n",
+       "  '\\n    This tool is a wrapper around atlassian-python-api\\'s Jira jql API, useful when you need to search for Jira issues.\\n    The input to this tool is a JQL query string, and will be passed into atlassian-python-api\\'s Jira `jql` function,\\n    For example, to find all the issues in project \"Test\" assigned to the me, you would pass in the following string:\\n    project = Test AND assignee = currentUser()\\n    or to find issues with summaries that contain the word \"test\", you would pass in the following string:\\n    summary ~ \\'test\\'\\n    '),\n",
+       " ('Get Projects',\n",
+       "  \"\\n    This tool is a wrapper around atlassian-python-api's Jira project API, \\n    useful when you need to fetch all the projects the user has access to, find out how many projects there are, or as an intermediary step that involv searching by projects. \\n    there is no input to this tool.\\n    \"),\n",
+       " ('Create Issue',\n",
+       "  '\\n    This tool is a wrapper around atlassian-python-api\\'s Jira issue_create API, useful when you need to create a Jira issue. \\n    The input to this tool is a dictionary specifying the fields of the Jira issue, and will be passed into atlassian-python-api\\'s Jira `issue_create` function.\\n    For example, to create a low priority task called \"test issue\" with description \"test description\", you would pass in the following dictionary: \\n    {{\"summary\": \"test issue\", \"description\": \"test description\", \"issuetype\": {{\"name\": \"Task\"}}, \"priority\": {{\"name\": \"Low\"}}}}\\n    '),\n",
+       " ('Catch all Jira API call',\n",
+       "  '\\n    This tool is a wrapper around atlassian-python-api\\'s Jira API.\\n    There are other dedicated tools for fetching all projects, and creating and searching for issues, \\n    use this tool if you need to perform any other actions allowed by the atlassian-python-api Jira API.\\n    The input to this tool is a dictionary specifying a function from atlassian-python-api\\'s Jira API, \\n    as well as a list of arguments and dictionary of keyword arguments to pass into the function.\\n    For example, to get all the users in a group, while increasing the max number of results to 100, you would\\n    pass in the following dictionary: {{\"function\": \"get_all_users_from_group\", \"args\": [\"group\"], \"kwargs\": {{\"limit\":100}} }}\\n    or to find out how many projects are in the Jira instance, you would pass in the following string:\\n    {{\"function\": \"projects\"}}\\n    For more information on the Jira API, refer to https://atlassian-python-api.readthedocs.io/jira.html\\n    '),\n",
+       " ('Create confluence page',\n",
+       "  'This tool is a wrapper around atlassian-python-api\\'s Confluence \\natlassian-python-api API, useful when you need to create a Confluence page. The input to this tool is a dictionary \\nspecifying the fields of the Confluence page, and will be passed into atlassian-python-api\\'s Confluence `create_page` \\nfunction. For example, to create a page in the DEMO space titled \"This is the title\" with body \"This is the body. You can use \\n<strong>HTML tags</strong>!\", you would pass in the following dictionary: {{\"space\": \"DEMO\", \"title\":\"This is the \\ntitle\",\"body\":\"This is the body. You can use <strong>HTML tags</strong>!\"}} ')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(tool.name, tool.description) for tool in toolkit.get_tools()]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -105,9 +189,6 @@
    },
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "jira = JiraAPIWrapper()\n",
-    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)\n",
     "agent = initialize_agent(\n",
     "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
     ")"

docs/docs/integrations/tools/json.ipynb

@@ -35,9 +35,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:52:40.107644Z",
+     "iopub.status.busy": "2024-10-02T17:52:40.107485Z",
+     "iopub.status.idle": "2024-10-02T17:52:40.110169Z",
+     "shell.execute_reply": "2024-10-02T17:52:40.109841Z",
+     "shell.execute_reply.started": "2024-10-02T17:52:40.107633Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -50,16 +57,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-02T17:59:54.134295Z",
+     "iopub.status.busy": "2024-10-02T17:59:54.134138Z",
+     "iopub.status.idle": "2024-10-02T17:59:54.137250Z",
+     "shell.execute_reply": "2024-10-02T17:59:54.136636Z",
+     "shell.execute_reply.started": "2024-10-02T17:59:54.134283Z"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "with open(\"openai_openapi.yml\") as f:\n",
     "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
+    "json_spec = JsonSpec(dict_={}, max_value_length=4000)\n",
     "json_toolkit = JsonToolkit(spec=json_spec)\n",
     "\n",
     "json_agent_executor = create_json_agent(\n",
@@ -67,6 +81,48 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "910eccbc-9d42-49b6-a4ca-1fbc418fcee7",
+   "metadata": {},
+   "source": [
+    "## Individual tools\n",
+    "\n",
+    "Let's see what individual tools are inside the Jira toolkit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b16a3ee5-ca16-452e-993f-c27228b945ac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-02T18:00:30.527665Z",
+     "iopub.status.busy": "2024-10-02T18:00:30.527053Z",
+     "iopub.status.idle": "2024-10-02T18:00:30.538483Z",
+     "shell.execute_reply": "2024-10-02T18:00:30.537672Z",
+     "shell.execute_reply.started": "2024-10-02T18:00:30.527626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('json_spec_list_keys',\n",
+       "  '\\n    Can be used to list all keys at a given path. \\n    Before calling this you should be SURE that the path to this exists.\\n    The input is a text representation of the path to the dict in Python syntax (e.g. data[\"key1\"][0][\"key2\"]).\\n    '),\n",
+       " ('json_spec_get_value',\n",
+       "  '\\n    Can be used to see value in string format at a given path.\\n    Before calling this you should be SURE that the path to this exists.\\n    The input is a text representation of the path to the dict in Python syntax (e.g. data[\"key1\"][0][\"key2\"]).\\n    ')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(el.name, el.description) for el in json_toolkit.get_tools()]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "05cfcb24-4389-4b8f-ad9e-466e3fca8db0",

docs/docs/integrations/tools/passio_nutrition_ai.ipynb

@@ -176,7 +176,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts#agents)"
+    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts#agents)"
    ]
   },
   {

docs/docs/integrations/tools/sql_database.ipynb

@@ -209,15 +209,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f5751e3-2e98-485f-8164-db8094039c25",
+   "id": "4e3fd064-aa86-448d-8db3-3c55eaa5bc15",
    "metadata": {},
    "source": [
-    "API references:\n",
-    "\n",
-    "- [QuerySQLDataBaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLDataBaseTool.html)\n",
-    "- [InfoSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool.html)\n",
-    "- [ListSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.ListSQLDatabaseTool.html)\n",
-    "- [QuerySQLCheckerTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html)"
+    "You can use the individual tools directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fa8d00c-750c-4803-9b66-057d12b26b06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools.sql_database.tool import (\n",
+    "    InfoSQLDatabaseTool,\n",
+    "    ListSQLDatabaseTool,\n",
+    "    QuerySQLCheckerTool,\n",
+    "    QuerySQLDataBaseTool,\n",
+    ")"
    ]
   },
   {
@@ -604,7 +614,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/google_spanner.ipynb

@@ -52,7 +52,7 @@
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet langchain-google-spanner"
+    "%pip install --upgrade --quiet langchain-google-spanner langchain-google-vertexai"
    ]
   },
   {
@@ -124,7 +124,8 @@
     "PROJECT_ID = \"my-project-id\"  # @param {type:\"string\"}\n",
     "\n",
     "# Set the project id\n",
-    "!gcloud config set project {PROJECT_ID}"
+    "!gcloud config set project {PROJECT_ID}\n",
+    "%env GOOGLE_CLOUD_PROJECT={PROJECT_ID}"
    ]
   },
   {
@@ -194,14 +195,16 @@
     "    instance_id=INSTANCE,\n",
     "    database_id=DATABASE,\n",
     "    table_name=TABLE_NAME,\n",
-    "    id_column=\"row_id\",\n",
-    "    metadata_columns=[\n",
-    "        TableColumn(name=\"metadata\", type=\"JSON\", is_null=True),\n",
-    "        TableColumn(name=\"title\", type=\"STRING(MAX)\", is_null=False),\n",
-    "    ],\n",
-    "    secondary_indexes=[\n",
-    "        SecondaryIndex(index_name=\"row_id_and_title\", columns=[\"row_id\", \"title\"])\n",
-    "    ],\n",
+    "    # Customize the table creation\n",
+    "    # id_column=\"row_id\",\n",
+    "    # content_column=\"content_column\",\n",
+    "    # metadata_columns=[\n",
+    "    #     TableColumn(name=\"metadata\", type=\"JSON\", is_null=True),\n",
+    "    #     TableColumn(name=\"title\", type=\"STRING(MAX)\", is_null=False),\n",
+    "    # ],\n",
+    "    # secondary_indexes=[\n",
+    "    #     SecondaryIndex(index_name=\"row_id_and_title\", columns=[\"row_id\", \"title\"])\n",
+    "    # ],\n",
     ")"
    ]
   },
@@ -262,9 +265,11 @@
     "    instance_id=INSTANCE,\n",
     "    database_id=DATABASE,\n",
     "    table_name=TABLE_NAME,\n",
-    "    ignore_metadata_columns=[],\n",
     "    embedding_service=embeddings,\n",
-    "    metadata_json_column=\"metadata\",\n",
+    "    # Connect to a custom vector store table\n",
+    "    # id_column=\"row_id\",\n",
+    "    # content_column=\"content\",\n",
+    "    # metadata_columns=[\"metadata\", \"title\"],\n",
     ")"
    ]
   },
@@ -272,7 +277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 🔐 Add Documents\n",
+    "#### Add Documents\n",
     "To add documents in the vector store."
    ]
   },
@@ -289,14 +294,15 @@
     "loader = HNLoader(\"https://news.ycombinator.com/item?id=34817881\")\n",
     "\n",
     "documents = loader.load()\n",
-    "ids = [str(uuid.uuid4()) for _ in range(len(documents))]"
+    "ids = [str(uuid.uuid4()) for _ in range(len(documents))]\n",
+    "db.add_documents(documents, ids)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 🔐 Search Documents\n",
+    "#### Search Documents\n",
     "To search documents in the vector store with similarity search."
    ]
   },
@@ -313,7 +319,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 🔐 Search Documents\n",
+    "#### Search Documents\n",
     "To search documents in the vector store with max marginal relevance search."
    ]
   },
@@ -330,7 +336,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 🔐 Delete Documents\n",
+    "#### Delete Documents\n",
     "To remove documents from the vector store, use the IDs that correspond to the values in the `row_id`` column when initializing the VectorStore."
    ]
   },
@@ -347,7 +353,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 🔐 Delete Documents\n",
+    "#### Delete Documents\n",
     "To remove documents from the vector store, you can utilize the documents themselves. The content column and metadata columns provided during VectorStore initialization will be used to find out the rows corresponding to the documents. Any matching rows will then be deleted."
    ]
   },
@@ -377,7 +383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

docs/docs/introduction.mdx

@@ -8,7 +8,7 @@ sidebar_class_name: hidden
 **LangChain** is a framework for developing applications powered by large language models (LLMs).
 
 LangChain simplifies every stage of the LLM application lifecycle:
-- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/platforms/).
+- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/providers/).
 Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).

docs/docs/security.md

@@ -6,7 +6,7 @@ LangChain has a large ecosystem of integrations with various external resources
 
 When building such applications developers should remember to follow good security practices:
 
-* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), etc. as appropriate for your application.
+* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
 * **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
 * [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
 

docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx

@@ -0,0 +1,14 @@
+# INVALID_PROMPT_INPUT
+
+A [prompt template](/docs/concepts#prompt-templates) received missing or invalid input variables.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Double-check your prompt template to ensure that it is correct.
+  - If you are using the default f-string format and you are using curly braces `{` anywhere in your template, they should be double escaped like this: `{{` (and if you want to render a double curly brace, you should use four curly braces: `{{{{`).
+- If you are using a [`MessagesPlaceholder`](/docs/concepts/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
+  - If you are using shorthand tuples to declare your prompt template, make sure that the variable name is wrapped in curly braces (`["placeholder", "{messages}"]`).
+- Try viewing the inputs into your prompt template using [LangSmith](https://docs.smith.langchain.com/) or log statements to confirm they appear as expected.
+- If you are pulling a prompt from the [LangChain Prompt Hub](https://smith.langchain.com/prompts), try pulling and logging it or running it in isolation with a sample input to confirm that it is what you expect.

docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb

@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MESSAGE_COERCION_FAILURE\n",
+    "\n",
+    "Instead of always requiring instances of `BaseMessage`, several modules in LangChain take `MessageLikeRepresentation`, which is defined as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Union\n",
+    "\n",
+    "from langchain_core.prompts.chat import (\n",
+    "    BaseChatPromptTemplate,\n",
+    "    BaseMessage,\n",
+    "    BaseMessagePromptTemplate,\n",
+    ")\n",
+    "\n",
+    "MessageLikeRepresentation = Union[\n",
+    "    Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate],\n",
+    "    tuple[\n",
+    "        Union[str, type],\n",
+    "        Union[str, list[dict], list[object]],\n",
+    "    ],\n",
+    "    str,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These include OpenAI style message objects (`{ role: \"user\", content: \"Hello world!\" }`),\n",
+    "tuples, and plain strings (which are converted to [`HumanMessages`](/docs/concepts#humanmessage)).\n",
+    "\n",
+    "If one of these modules receives a value outside of one of these formats, you will receive an error like the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Message dict must contain 'role' and 'content' keys, got {'role': 'HumanMessage', 'random_field': 'random value'}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:318\u001b[0m, in \u001b[0;36m_convert_to_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;66;03m# None msg content is not allowed\u001b[39;00m\n\u001b[0;32m--> 318\u001b[0m     msg_content \u001b[38;5;241m=\u001b[39m \u001b[43mmsg_kwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'content'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m      3\u001b[0m uncoercible_message \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHumanMessage\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_field\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom value\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      6\u001b[0m }\n\u001b[1;32m      8\u001b[0m model \u001b[38;5;241m=\u001b[39m ChatAnthropic(model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclaude-3-5-sonnet-20240620\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43muncoercible_message\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:287\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    277\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    283\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    285\u001b[0m         ChatGeneration,\n\u001b[1;32m    286\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m--> 287\u001b[0m             [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m],\n\u001b[1;32m    288\u001b[0m             stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[1;32m    289\u001b[0m             callbacks\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    290\u001b[0m             tags\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    291\u001b[0m             metadata\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    292\u001b[0m             run_name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    293\u001b[0m             run_id\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m    294\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    295\u001b[0m         )\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    296\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:267\u001b[0m, in \u001b[0;36mBaseChatModel._convert_input\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m StringPromptValue(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, Sequence):\n\u001b[0;32m--> 267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ChatPromptValue(messages\u001b[38;5;241m=\u001b[39m\u001b[43mconvert_to_messages\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    269\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    270\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid input type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28minput\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust be a PromptValue, str, or list of BaseMessages.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    272\u001b[0m     )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:348\u001b[0m, in \u001b[0;36mconvert_to_messages\u001b[0;34m(messages)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(messages, PromptValue):\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m messages\u001b[38;5;241m.\u001b[39mto_messages()\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\u001b[43m_convert_to_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m]\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:348\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(messages, PromptValue):\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m messages\u001b[38;5;241m.\u001b[39mto_messages()\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43m_convert_to_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m messages]\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:321\u001b[0m, in \u001b[0;36m_convert_to_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    320\u001b[0m         msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMessage dict must contain \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keys, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 321\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     _message \u001b[38;5;241m=\u001b[39m _create_message_from_message_type(\n\u001b[1;32m    323\u001b[0m         msg_type, msg_content, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg_kwargs\n\u001b[1;32m    324\u001b[0m     )\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[0;31mValueError\u001b[0m: Message dict must contain 'role' and 'content' keys, got {'role': 'HumanMessage', 'random_field': 'random value'}"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "uncoercible_message = {\"role\": \"HumanMessage\", \"random_field\": \"random value\"}\n",
+    "\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
+    "\n",
+    "model.invoke([uncoercible_message])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Troubleshooting\n",
+    "\n",
+    "The following may help resolve this error:\n",
+    "\n",
+    "- Ensure that all inputs to chat models are an array of LangChain message classes or a supported message-like.\n",
+    "  - Check that there is no stringification or other unexpected transformation occuring.\n",
+    "- Check the error's stack trace and add log or debugger statements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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
+}

docs/docs/troubleshooting/errors/MODEL_AUTHENTICATION.mdx

@@ -0,0 +1,18 @@
+# MODEL_AUTHENTICATION
+
+Your model provider is denying you access to their service.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Confirm that your API key or other credentials are correct.
+- If you are relying on an environment variable to authenticate, confirm that the variable name is correct and that it has a value set.
+  - Note that environment variables can also be set by packages like `dotenv`.
+  - For models, you can try explicitly passing an `api_key` parameter to rule out any environment variable issues like this:
+
+```python
+model = ChatOpenAI(api_key="YOUR_KEY_HERE")
+```
+
+- If you are using a proxy or other custom endpoint, make sure that your custom provider does not expect an alternative authentication scheme.

docs/docs/troubleshooting/errors/MODEL_NOT_FOUND.mdx

@@ -0,0 +1,10 @@
+# MODEL_NOT_FOUND
+
+The model name you have specified is not acknowledged by your provider.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Double check the model string you are passing in.
+- If you are using a proxy or other alternative host with a model wrapper, confirm that the permitted model names are not restricted or altered.

docs/docs/troubleshooting/errors/MODEL_RATE_LIMIT.mdx

@@ -0,0 +1,13 @@
+# MODEL_RATE_LIMIT
+
+You have hit the maximum number of requests that a model provider allows over a given time period and are being temporarily blocked.
+Generally, this error is temporary and your limit will reset after a certain amount of time.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Contact your model provider and ask for a rate limit increase.
+- If many of your incoming requests are the same, utilize [model response caching](/docs/how_to/chat_model_caching/).
+- Spread requests across different providers if your application allows it.
+- Use a [`rate_limiter`](/docs/how_to/chat_model_rate_limiting/) to control the rate of requests to the model.

docs/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# OUTPUT_PARSING_FAILURE\n",
+    "\n",
+    "An [output parser](/docs/concepts#output-parsers) was unable to handle model output as expected.\n",
+    "\n",
+    "To illustrate this, let's say you have an output parser that expects a chat model to output JSON surrounded by a markdown code tag (triple backticks). Here would be an example of good input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'foo': 'bar'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "message = AIMessage(content='```\\n{\"foo\": \"bar\"}\\n```')\n",
+    "output_parser = JsonOutputParser()\n",
+    "output_parser.invoke(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Internally, our JSON parser stripped out the markdown fence and newlines and then ran `json.loads`.\n",
+    "\n",
+    "If instead the chat model generated an output with malformed JSON, we will get an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OutputParserException",
+     "evalue": "Invalid json output: ```\n{{\"foo\":\n```",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/json.py:83\u001b[0m, in \u001b[0;36mJsonOutputParser.parse_result\u001b[0;34m(self, result, partial)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 83\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparse_json_markdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:144\u001b[0m, in \u001b[0;36mparse_json_markdown\u001b[0;34m(json_string, parser)\u001b[0m\n\u001b[1;32m    143\u001b[0m     json_str \u001b[38;5;241m=\u001b[39m json_string \u001b[38;5;28;01mif\u001b[39;00m match \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m match\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_parse_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparser\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:160\u001b[0m, in \u001b[0;36m_parse_json\u001b[0;34m(json_str, parser)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# Parse the JSON string into a Python dictionary\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:118\u001b[0m, in \u001b[0;36mparse_partial_json\u001b[0;34m(s, strict)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# If we got here, we ran out of characters to remove\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;66;03m# and still couldn't parse the string as JSON, so return the parse error\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;66;03m# for the original string.\u001b[39;00m\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/__init__.py:359\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    358\u001b[0m     kw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_constant\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m parse_constant\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/decoder.py:353\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 353\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mOutputParserException\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m message \u001b[38;5;241m=\u001b[39m AIMessage(content\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m```\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m{{\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfoo\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m```\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m output_parser \u001b[38;5;241m=\u001b[39m JsonOutputParser()\n\u001b[0;32m----> 3\u001b[0m \u001b[43moutput_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/base.py:193\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    188\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, BaseMessage],\n\u001b[1;32m    189\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    191\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[0;32m--> 193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    194\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[1;32m    203\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparse_result([Generation(text\u001b[38;5;241m=\u001b[39minner_input)]),\n\u001b[1;32m    204\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    205\u001b[0m             config,\n\u001b[1;32m    206\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    207\u001b[0m         )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:1927\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, serialized, **kwargs)\u001b[0m\n\u001b[1;32m   1923\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m   1924\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[1;32m   1925\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m   1926\u001b[0m         Output,\n\u001b[0;32m-> 1927\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1928\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1929\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1930\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1931\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1932\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1933\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1934\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1935\u001b[0m     )\n\u001b[1;32m   1936\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1937\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/config.py:396\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    395\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/base.py:194\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke.<locals>.<lambda>\u001b[0;34m(inner_input)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    188\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, BaseMessage],\n\u001b[1;32m    189\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    191\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[1;32m    193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[0;32m--> 194\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    197\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    198\u001b[0m             config,\n\u001b[1;32m    199\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    200\u001b[0m         )\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[1;32m    203\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparse_result([Generation(text\u001b[38;5;241m=\u001b[39minner_input)]),\n\u001b[1;32m    204\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    205\u001b[0m             config,\n\u001b[1;32m    206\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    207\u001b[0m         )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/json.py:86\u001b[0m, in \u001b[0;36mJsonOutputParser.parse_result\u001b[0;34m(self, result, partial)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     85\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid json output: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 86\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(msg, llm_output\u001b[38;5;241m=\u001b[39mtext) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "\u001b[0;31mOutputParserException\u001b[0m: Invalid json output: ```\n{{\"foo\":\n```"
+     ]
+    }
+   ],
+   "source": [
+    "message = AIMessage(content='```\\n{{\"foo\":\\n```')\n",
+    "output_parser = JsonOutputParser()\n",
+    "output_parser.invoke(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that some prebuilt constructs like [legacy LangChain agents](/docs/how_to/agent_executor) and chains may use output parsers internally,\n",
+    "so you may see this error even if you're not visibly instantiating and using an output parser.\n",
+    "\n",
+    "## Troubleshooting\n",
+    "\n",
+    "The following may help resolve this error:\n",
+    "\n",
+    "- Consider using [tool calling or other structured output techniques](/docs/how_to/structured_output/) if possible without an output parser to reliably output parseable values.\n",
+    "  - If you are using a prebuilt chain or agent, use [LangGraph](https://langchain-ai.github.io/langgraph/) to compose your logic explicitly instead.\n",
+    "- Add more precise formatting instructions to your prompt. In the above example, adding `\"You must always return valid JSON fenced by a markdown code block. Do not return any additional text.\"` to your input may help steer the model to returning the expected format.\n",
+    "- If you are using a smaller or less capable model, try using a more capable one.\n",
+    "- Add [LLM-powered retries](/docs/how_to/output_parser_fixing/)."
+   ]
+  }
+ ],
+ "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
+}

docs/docs/troubleshooting/errors/index.mdx

@@ -0,0 +1,12 @@
+# Error reference
+
+This page contains guides around resolving common errors you may find while building with LangChain.
+Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
+
+- [INVALID_PROMPT_INPUT](/docs/troubleshooting/errors/INVALID_PROMPT_INPUT)
+- [INVALID_TOOL_RESULTS](/docs/troubleshooting/errors/INVALID_TOOL_RESULTS)
+- [MESSAGE_COERCION_FAILURE](/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE)
+- [MODEL_AUTHENTICATION](/docs/troubleshooting/errors/MODEL_AUTHENTICATION)
+- [MODEL_NOT_FOUND](/docs/troubleshooting/errors/MODEL_NOT_FOUND)
+- [MODEL_RATE_LIMIT](/docs/troubleshooting/errors/MODEL_RATE_LIMIT)
+- [OUTPUT_PARSING_FAILURE](/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE)

docs/docs/tutorials/chatbot.ipynb

@@ -438,7 +438,7 @@
     "app = workflow.compile(checkpointer=MemorySaver())\n",
     "\n",
     "# Async invocation:\n",
-    "output = await app.ainvoke({\"messages\": input_messages}, config):\n",
+    "output = await app.ainvoke({\"messages\": input_messages}, config)\n",
     "output[\"messages\"][-1].pretty_print()\n",
     "```\n",
     "\n",
@@ -449,7 +449,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Right now, all we've done is add a simple persistence layer around the model. We can start to make the more complicated and personalized by adding in a prompt template.\n",
+    "Right now, all we've done is add a simple persistence layer around the model. We can start to make the chatbot more complicated and personalized by adding in a prompt template.\n",
     "\n",
     "## Prompt templates\n",
     "\n",
@@ -686,7 +686,7 @@
     "\n",
     "input_messages = [HumanMessage(query)]\n",
     "output = app.invoke(\n",
-    "    {\"messages\": input_messages, \"language\": language},\n",
+    "    {\"messages\": input_messages},\n",
     "    config,\n",
     ")\n",
     "output[\"messages\"][-1].pretty_print()"

docs/docs/tutorials/llm_chain.ipynb

@@ -497,15 +497,14 @@
     "# 4. Create chain\n",
     "chain = prompt_template | model | parser\n",
     "\n",
-    "\n",
-    "# 4. App definition\n",
+    "# 5. App definition\n",
     "app = FastAPI(\n",
     "  title=\"LangChain Server\",\n",
     "  version=\"1.0\",\n",
     "  description=\"A simple API server using LangChain's Runnable interfaces\",\n",
     ")\n",
     "\n",
-    "# 5. Adding chain route\n",
+    "# 6. Adding chain route\n",
     "add_routes(\n",
     "    app,\n",
     "    chain,\n",