Merge branch 'master' into pprados/06-pdfplumber

.github/workflows/_integration_test.yml

@@ -6,6 +6,7 @@ on:
       working-directory:
         required: true
         type: string
+        description: "From which folder this pipeline executes"
       python-version:
         required: true
         type: string

.github/workflows/_release.yml

@@ -12,6 +12,7 @@ on:
       working-directory:
         required: true
         type: string
+        description: "From which folder this pipeline executes"
         default: 'libs/langchain'
       dangerous-nonmaster-release:
         required: false
@@ -336,7 +337,6 @@ jobs:
       - release-notes
       - test-pypi-publish
       - pre-release-checks
-    if: ${{ startsWith(inputs.working-directory, 'libs/core') }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -355,17 +355,29 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
+      # We implement this conditional as Github Actions does not have good support
+      # for conditionally needing steps. https://github.com/actions/runner/issues/491
+      - name: Check if libs/core
+        run: |
+          if [ "${{ startsWith(inputs.working-directory, 'libs/core') }}" != "true" ]; then
+            echo "Not in libs/core. Exiting successfully."
+            exit 0
+          fi
+
       - name: Set up Python + uv
+        if: startsWith(inputs.working-directory, 'libs/core')
         uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
       - uses: actions/download-artifact@v4
+        if: startsWith(inputs.working-directory, 'libs/core')
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
 
       - name: Test against ${{ matrix.partner }}
+        if: startsWith(inputs.working-directory, 'libs/core')
         run: |
           # Identify latest tag
           LATEST_PACKAGE_TAG="$(
@@ -401,15 +413,6 @@ jobs:
       - test-pypi-publish
       - pre-release-checks
       - test-prior-published-packages-against-new-core
-    if: >
-      always() &&
-      needs.build.result == 'success' &&
-      needs.release-notes.result == 'success' &&
-      needs.test-pypi-publish.result == 'success' &&
-      needs.pre-release-checks.result == 'success' && (
-        (startsWith(inputs.working-directory, 'libs/core') && needs.test-prior-published-packages-against-new-core.result == 'success')
-        || (!startsWith(inputs.working-directory, 'libs/core'))
-      )
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:

.github/workflows/check_diffs.yml

@@ -1,4 +1,3 @@
----
 name: CI
 
 on:

.github/workflows/check_new_docs.yml

@@ -1,4 +1,3 @@
----
 name: Integration docs lint
 
 on:

.github/workflows/codespell.yml

@@ -1,4 +1,3 @@
----
 name: CI / cd . / make spell_check
 
 on:

.readthedocs.yaml

@@ -1,12 +1,7 @@
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
 version: 2
 
-formats:
-  - pdf
-
 # Set the version of Python and other tools you might need
 build:
   os: ubuntu-22.04
@@ -15,13 +10,14 @@ build:
   commands:
     - mkdir -p $READTHEDOCS_OUTPUT
     - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
    configuration: docs/api_reference/conf.py
 
 # If using Sphinx, optionally build your docs in additional formats such as PDF
-# formats:
-#    - pdf
+formats:
+  - pdf
 
 # Optionally declare the Python requirements required to build your docs
 python:

cookbook/Gemma_LangChain.ipynb

@@ -60,7 +60,7 @@
     "id": "CI8Elyc5gBQF"
    },
    "source": [
-    "Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint it ready, you need to copy its number."
+    "Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint is ready, you need to copy its number."
    ]
   },
   {

docs/cassettes/qa_chat_history_4fbca953-970d-4271-be30-6c7799893dd1.msgpack.zlib

@@ -1 +1 @@
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

docs/cassettes/qa_chat_history_how_to_e59541dd-405b-4032-847f-73becf6aefd0.msgpack.zlib

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docs/cassettes/qa_citations_daad793f-f08d-4b56-90e8-3f015a79a88e.msgpack.zlib

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docs/cassettes/qa_sources_def66576-2309-45b5-8699-f0287df8ca9e.msgpack.zlib

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docs/cassettes/qa_streaming_249e71c6-6cc5-40c0-b58f-7ec949c6a28f.msgpack.zlib

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docs/cassettes/rag_46f378c5-858c-488f-8aef-8b59a6280791.msgpack.zlib

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docs/cassettes/response_metadata_161f5898-9976-4a75-943d-03eda1a40a60.msgpack.zlib

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docs/cassettes/response_metadata_1e4ac668-4c6a-48ad-9a6f-7b291477b45d.msgpack.zlib

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docs/cassettes/response_metadata_39549336-25f5-4839-9846-f687cd77e59b.msgpack.zlib

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docs/cassettes/response_metadata_61c43496-83b5-4d71-bd60-3e6d46c62a5e.msgpack.zlib

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docs/cassettes/response_metadata_6ae32a93-26db-41bb-95c2-38ddd5085fbe.msgpack.zlib

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docs/cassettes/response_metadata_744e14ec-ff50-4642-9893-ff7bdf8927ff.msgpack.zlib

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docs/cassettes/response_metadata_a984118e-a731-4864-bcea-7dc6c6b3d139.msgpack.zlib

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docs/cassettes/response_metadata_deb41321-52d0-4795-a40c-4a811a13d7b0.msgpack.zlib

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docs/cassettes/sql_qa_11.msgpack.zlib

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docs/cassettes/sql_qa_39.msgpack.zlib

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docs/cassettes/summarize_refine_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib

@@ -1 +1 @@
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+eNrtmAtUE1cagMEX3ZajaH2gtRqjrdUykEmGkATjlvfL8BaQlnImMzdkYDITZiYJQVsV7NqtUhuxuuoRFZBQfBfQWlCrFqUVX109iCBoqV0R8dnWZ3UvAVqs9uzprp5T9zjAhJn73//+z3u/kxy7GXA8xTLOGyhGABxOCPCBz8+xcyDTBHhhXokBCHqWLI6OiosvMnFUw2S9IBh5lZcXbqQ8WSNgcMqTYA1eZtSL0OOCF/zfSAOHmmItS1obVswUGwDP42mAF6venCkmWLgSI4hV4kSOEoAIF8E3BMUDEW8yGHDOKmJ1IkEPRDqWplkLxaSpRH5GqJMX4RwQcYAUe4g5lgZQg4kHnPidFA+xgSUBDV+kGQUEYxEDxVBQihc4gBvEKh1O88BDLACDEfoomDg4V+IpeceuBzgJA7CoWM/ygm3Tgy5txgkCQH2AIVgSmmHbmJZNGT1EJNDRuADKoNkMcATMVpYBgBHBacoMSrpm2bbg0GaKwDvHvdJ5ltnQ7TgiWI3g4eGyTl8QGCVGsO3w460MEQUt8QvzirbCBDAi1FMu95RuyUJ4AacYGA0eoXFoVInRMV7Ve8CIExlQE9KdXFtJ1+RNvWVY3rZOgxNRcQ+oxDlCb1uHcwY5Vt77PWdiBMoAbPaA6IeX6x78ZTm7zBNF4e/WBzR3umTb6PhQOe4Uu/0BLUDgrAjBQmW2tZJNPdGiAZMm6G1FqDdWygHeCOsK5JbAaYKJzymGmQF1tfbuAiuMiuhJabPTiOJAmCXbzjhc8BBJpSINzomkEqm3CJWrMEwlVYhCNPEbArqXiX9kUrbGczjD62BignqKwE7oTUwGIMsCHpn+nZ3ph950mg+LFAFZRpYHSLdVtg1JSGxXZyFhgeVdtYawXBrOUNmOZW07HXVgyc6ykISJJPVmi0GizMZklBaYCF1F9xQjx3YuAw1CDLytSKb03tQ90pOEMuirBEEliAT9PAuBVQ9oykDBeDru3e3N24q9JRLJZw8LCGwGYHibHZM4rl29JThggEnrXPtXNZhSqax+tFCPKhkUUSqwzx+U4kFva1Cpgf/sYYFuFYUSfkNWjzRCkbaGCfAhVSIFKEqgSgnmjcowH4VCopMrMFymwHVAQnrLd8B9gCKgls5kGllOQHhAwL1MsNoaPAx4VmfXqWWot0wOPfUVUQxBm0gQZ9IGsp0+8L4iIwdoFic3EzqEwAk9QLrqz2YPnBHppwkLKIuDRgawbAYFFp9ydk9NJXSpWoPa2y+ZNgbLZIHmMEsmhZKJAAPpcotVb5RjiYn+EenSCGNGMsWGUSyC+mBSuRxVyBUI6inxhP2DBCVpgk1MDCCUxlgq05idFKr1jCbN/qbp1oDg8NRgf395FBNLyBnam06T6mmD4B+gxAhvE+tDE4lRmZ7SCGw6GZsRLIRykVh2Km3Q6IMysyISSSI9xlPGhweQmVmmZCmR5gddxAW92stXBAuWgkFXd7cNAtsG6WwaHxXa0zS+ItIRGLXngxumrygUng1RDG31FcV1RhjAT9wA4uA2r45kGdCwBAbGZKZIdURqQEgCF6aXahKn6xQR8XItGZBtDQ8IkEuz6HAuTebn5x/vJ50RGjajV2R8UAUi6Q6OXIIpHKX5q+n/pVXbkpDeuwASZXScXjY7w/IMpdOVxAEOdpWtjKBZEwn3fg6UBAQjsX4zbBVKKeYj9ZFjGIFLMVLijfjDDbVH2y97RnHnwWHHaVh4ZsJWrpepxXALkol9RQZcrZDDHnMclXNLOguVSatxrhi74Dknx9UX/t2/v3CxH9socX3v8r1hOYczbf1mfPv9tsI2vMrji88PRrsvDPRNBzEfnffws92fWhtJWNrnpMw6cyBkd8hV7xf9RyH1/mXqLenVhy5fmq2mz14pShKxKw60Jq73adk1dnvq8dbTCZGLc8xHSm9qTEWlV4c3fNS+9fnnFlIX6Mp/eC0r184Q1ozLk3+VfuRWzrX9ivwj9utBU1POlX9b2Jwf434u+W7dPLdvK++8ujqyNWZTjXAtgZIEl8Zs2qzxr31DPn3Cl3pxLqEcuUZ84LyqffXO7e79+w2ddS14buXEwdEvtM4TR5zVebgmVQ5ebK1P9u1zZjRafGXVOfsLylRNctk7a1LuBkx+vXVa0zLncYsj/ibOX/ayOs1/iumgvuVc/tTo1h1k9p5LlvLCsRPnT3Q/0/Eeip5cOyVVM9U9IZ2UyD80TxpWqr7gp/QYk2zQ9/s05rvdO2dhVYsMb/3811aXNUOzg1NuzzlVesM6f9uctqn3T5hTMtOSgl1diraQuePUufufT25MzVtorz255ONM7wFjp81WfexlqYk8/W7OpNeWrypvcaSmr9ORPQf7h8I8PUZoOuWc+bvU9BYjglc0x5ImopOedBSD0z3s5Nk12nUPyqJ4AdbTL2BlMooEFrIVxYuMLAQ+VZdcL7qCbALPP5q2dnIW3Akhm9Es94DWSGAROazJ6pmP9Lq63vjTJqAFHEd1q9XC59+T7bqHwLOTcWAf86t+D2gFdA843rMclfYIVx87DDY7uT3DwT8bDpYQjsPVdsrZ5Wk8XJ/AsfcQIMvkyj8GyMP/AyAr/48AGcOUTwkgy2T/AyAXoYZHE7ISk2JKrRaXyDBUhgJUIlOgGIorSUKCSQn0yRPyY4AsH6CV4Y8Rsvb9FrJiNRlNEreddxJdVY14h6rqYHNs+F/XRGefX/eK+0Zsm93m7b843xO/P9NmKTef6sehWc3V9hu679G+bsU1A+ddAPG5spo7gwaN3b2y+eVdP7QwN/nzs99e1FofdTWk5oZFHOXafD942OqkxA9c0iRDGl/cMOXr98Mqm46fyE5h6hv/4p5UFFZxuKbwe60oIqiy7IK2ctLXHctPyya5al12S53mHGhbOSs5b/aXAxNuxYEv3Mj5WgvVx61AU/bcyD7vvWYYest18rbBHeTP+5W5zsUh77uEhI/inRaNqs3R7ps6f36B697mhZ+FXG5rWnVv89LCMY1o7dC6D/d7OFFIyPIBspod6vEHIpz2rbpePeh+dFDEUv+WrCVzDdKXxL7o2gOuZ8yjjt/0qDIWuIxYWWtRqLd7RnHat4+4Lv1x5MLR65a3r2y/278ymFhC7tUe9Q87UVSw/HrCd6V/H/HGttq7u5cvq4ja8ZWZta12Xnc06Hje9dIw9aAp263TDtxJbgT78/HBR1vy97465HaFS97G0YfaigMV894P+OfijqO+Ufaq9JRc0+sthoSPmSBz3b0+XcxlaUv0BY+buT74MzGXh8iip+gudPotSv1xHsMZ+NOlwAob1fIMxp7B2DMYe4IwpsSewdjvwpjiafm2UurzBGAM91H6aFESkyuBTAcwOUH6oHJCCSCWSQmcfDpgTKtUSrDHCGOHfwtjcXvSHTD2+oBVHVuqTGafmRGvret45ejwPsieheuubkqeMX7CjrYhP7UsoNM6iid9pz1abS260Vy7aOSco+EX36jtG9ygimuqnn3zSv93Ru7y3JX67u0sy0Gf0PaqxvVHLr051Ko1/pxPztKmhDKr1pY2DNzywzzbN8fPlby1sf5gx+0yl+QEcojhsjaGO5a+fsEJ7fThCzRbmz682DcjzO/imZABTldOzs6PXH9+0ck94tllFUP7XAq7eFjjtLc8bNjccefdjg1cERL9aR45s63+8Mjn5xSIxqxaqq8bPGXS0eETXvpJebykaEV1PZGq+tecBL+84cjAE+NH7hs04nrGFO+UL8gT5v11ThVLx5eUZufeGM+p3pRWWwrar+RMHmUwKeILJFs01wLIidk/LHFuunHMdOQbt8N8u3bYrRE/1m/02adAEbrPssbBC0cfm/ZjB7j3dUV4TKBhr0f2uJgxoZo6KurkpEMy0UrrvcS6jHKXllOXWqTv9imeMCJs+uH8b67tOPvJobjsw/czfNOaXhg+9y6LfPLBilmncgsH+MruuZ91F1dOL+ooqL6enUXlrS+lr5zWbY3Nq41sSO3+LuwrarPcAjP4b+ZKJy4=

docs/docs/concepts/structured_outputs.mdx

@@ -16,7 +16,7 @@ This need motivates the concept of structured output, where models can be instru
 
 ## Recommended usage
 
-This pseudo-code illustrates the recommended workflow when using structured output.
+This pseudocode illustrates the recommended workflow when using structured output.
 LangChain provides a method, [`with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method), that automates the process of binding the schema to the [model](/docs/concepts/chat_models/) and parsing the output.
 This helper function is available for all model providers that support structured output. 
 
@@ -99,20 +99,10 @@ Here is an example of how to use JSON mode with OpenAI:
 
 ```python
 from langchain_openai import ChatOpenAI
-model = ChatOpenAI(model="gpt-4o", model_kwargs={ "response_format": { "type": "json_object" } })
+model = ChatOpenAI(model="gpt-4o").with_structured_output(method="json_mode")
 ai_msg = model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
-ai_msg.content
-'\n{\n  "random_ints": [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]\n}'
-```
-
-One important point to flag: the model *still* returns a string, which needs to be parsed into a JSON object.
-This can, of course, simply use the `json` library or a JSON output parser if you need more advanced functionality.
-See this [how-to guide on the JSON output parser](/docs/how_to/output_parser_json) for more details.
-
-```python
-import json
-json_object = json.loads(ai_msg.content)
-{'random_ints': [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]}
+ai_msg
+{'random_ints': [45, 67, 12, 34, 89, 23, 78, 56, 90, 11]}
 ```
 
 ## Structured output method 

docs/docs/concepts/tool_calling.mdx

@@ -30,7 +30,7 @@ You will sometimes hear the term `function calling`. We use this term interchang
 
 ## Recommended usage
 
-This pseudo-code illustrates the recommended workflow for using tool calling. 
+This pseudocode illustrates the recommended workflow for using tool calling.
 Created tools are passed to `.bind_tools()` method as a list.
 This model can be called, as usual. If a tool call is made, model's response will contain the tool call arguments.
 The tool call arguments can be passed directly to the tool.

docs/docs/contributing/how_to/integrations/index.mdx

@@ -73,17 +73,18 @@ In order to contribute an integration, you should follow these steps:
 
 ## Co-Marketing
 
-With over 20 million monthly downloads, LangChain has a large audience of developers building LLM applications.
-Besides just adding integrations, we also like to show them examples of cool tools or APIs they can use.
+With over 20 million monthly downloads, LangChain has a large audience of developers
+building LLM applications. Beyond just listing integrations, we aim to highlight
+high-quality, educational examples that inspire developers and advance the ecosystem.
 
-While traditionally called "co-marketing", we like to think of this more as "co-education".
-For that reason, while we are happy to highlight your integration through our social media channels, we prefer to highlight examples that also serve some educational purpose.
-Our main social media channels are Twitter and LinkedIn.
+While we occasionally share integrations, we prioritize content that provides
+meaningful insights and best practices. Our main social channels are Twitter and
+LinkedIn, where we highlight the best examples.
 
 Here are some heuristics for types of content we are excited to promote:
 
-- **Integration announcement:** If you announce the integration with a link to the LangChain documentation page, we are happy to re-tweet/re-share on Twitter/LinkedIn.
-- **Educational content:** We highlight good educational content on the weekends - if you write a good blog or make a good YouTube video, we are happy to share there! Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers.
+- **Integration announcement:** Announcements of new integrations with a link to the LangChain documentation page.
+- **Educational content:** Blogs, YouTube videos and other media showcasing educational content. Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers.
 - **End-to-end applications:** End-to-end applications are great resources for developers looking to build. We prefer to highlight applications that are more complex/agentic in nature, and that use [LangGraph](https://github.com/langchain-ai/langgraph) as the orchestration framework. We get particularly excited about anything involving long-term memory, human-in-the-loop interaction patterns, or multi-agent architectures.
 - **Research:** We love highlighting novel research! Whether it is research built on top of LangChain or that integrates with it.
 

docs/docs/contributing/how_to/integrations/package.mdx

@@ -35,7 +35,7 @@ from a template, which can be edited to implement your LangChain components.
 - [GitHub](https://github.com) account
 - [PyPi](https://pypi.org/) account
 
-### Boostrapping a new Python package with langchain-cli
+### Bootstrapping a new Python package with langchain-cli
 
 First, install `langchain-cli` and `poetry`:
 
@@ -104,7 +104,7 @@ dependency management and packaging, and you're welcome to use any other tools y
 - [GitHub](https://github.com) account
 - [PyPi](https://pypi.org/) account
 
-### Boostrapping a new Python package with Poetry
+### Bootstrapping a new Python package with Poetry
 
 First, install Poetry:
 

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -29,6 +29,31 @@
     "%pip install -qU langchain-anthropic langchain-openai"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c71b676a",
+   "metadata": {},
+   "source": [
+    ":::note A note on streaming with OpenAI\n",
+    "\n",
+    "OpenAI's Chat Completions API does not stream token usage statistics by default (see API reference\n",
+    "[here](https://platform.openai.com/docs/api-reference/completions/create#completions-create-stream_options)).\n",
+    "To recover token counts when streaming with `ChatOpenAI`, set `stream_usage=True` as\n",
+    "demonstrated in this guide.\n",
+    "\n",
+    "For `AzureChatOpenAI`, set `stream_options={\"include_usage\": True}` when calling\n",
+    "`.(a)stream`, or initialize with:\n",
+    "\n",
+    "```python\n",
+    "AzureChatOpenAI(\n",
+    "    ...,\n",
+    "    model_kwargs={\"stream_options\": {\"include_usage\": True}},\n",
+    ")\n",
+    "```\n",
+    "\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "598ae1e2-a52d-4459-81fd-cdc68b06742a",

docs/docs/how_to/document_loader_html.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "This covers how to load `HTML` documents into a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
     "\n",
-    "Parsing HTML files often requires specialized tools. Here we demonstrate parsing via [Unstructured](https://unstructured-io.github.io/unstructured/) and [BeautifulSoup4](https://beautiful-soup-4.readthedocs.io/en/latest/), which can be installed via pip. Head over to the integrations page to find integrations with additional services, such as [Azure AI Document Intelligence](/docs/integrations/document_loaders/azure_document_intelligence) or [FireCrawl](/docs/integrations/document_loaders/firecrawl).\n",
+    "Parsing HTML files often requires specialized tools. Here we demonstrate parsing via [Unstructured](https://docs.unstructured.io) and [BeautifulSoup4](https://beautiful-soup-4.readthedocs.io/en/latest/), which can be installed via pip. Head over to the integrations page to find integrations with additional services, such as [Azure AI Document Intelligence](/docs/integrations/document_loaders/azure_document_intelligence) or [FireCrawl](/docs/integrations/document_loaders/firecrawl).\n",
     "\n",
     "## Loading HTML with Unstructured"
    ]

docs/docs/how_to/response_metadata.ipynb

@@ -16,23 +16,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "161f5898-9976-4a75-943d-03eda1a40a60",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'token_usage': {'completion_tokens': 164,\n",
-       "  'prompt_tokens': 17,\n",
-       "  'total_tokens': 181},\n",
-       " 'model_name': 'gpt-4-turbo',\n",
-       " 'system_fingerprint': 'fp_76f018034d',\n",
+       "{'token_usage': {'completion_tokens': 110,\n",
+       "  'prompt_tokens': 16,\n",
+       "  'total_tokens': 126,\n",
+       "  'completion_tokens_details': {'accepted_prediction_tokens': 0,\n",
+       "   'audio_tokens': 0,\n",
+       "   'reasoning_tokens': 0,\n",
+       "   'rejected_prediction_tokens': 0},\n",
+       "  'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}},\n",
+       " 'model_name': 'gpt-4o-mini-2024-07-18',\n",
+       " 'system_fingerprint': 'fp_b8bc95a0ac',\n",
+       " 'id': 'chatcmpl-BDrISvLar6AqcZngBmhajFZXVc2u9',\n",
        " 'finish_reason': 'stop',\n",
        " 'logprobs': None}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -40,8 +46,8 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4-turbo\")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   },
@@ -55,21 +61,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "61c43496-83b5-4d71-bd60-3e6d46c62a5e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'id': 'msg_01CzQyD7BX8nkhDNfT1QqvEp',\n",
-       " 'model': 'claude-3-sonnet-20240229',\n",
+       "{'id': 'msg_01JHnvPqgERY7MZwrvfkmq52',\n",
+       " 'model': 'claude-3-5-sonnet-20241022',\n",
        " 'stop_reason': 'end_turn',\n",
        " 'stop_sequence': None,\n",
-       " 'usage': {'input_tokens': 17, 'output_tokens': 296}}"
+       " 'usage': {'cache_creation_input_tokens': 0,\n",
+       "  'cache_read_input_tokens': 0,\n",
+       "  'input_tokens': 17,\n",
+       "  'output_tokens': 221},\n",
+       " 'model_name': 'claude-3-5-sonnet-20241022'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,8 +87,8 @@
    "source": [
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
-    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   },
@@ -92,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "39549336-25f5-4839-9846-f687cd77e59b",
    "metadata": {},
    "outputs": [
@@ -102,23 +112,41 @@
        "{'is_blocked': False,\n",
        " 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
        "   'probability_label': 'NEGLIGIBLE',\n",
-       "   'blocked': False},\n",
+       "   'probability_score': 0.046142578125,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.07275390625},\n",
        "  {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
        "   'probability_label': 'NEGLIGIBLE',\n",
-       "   'blocked': False},\n",
+       "   'probability_score': 0.05419921875,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.03955078125},\n",
        "  {'category': 'HARM_CATEGORY_HARASSMENT',\n",
        "   'probability_label': 'NEGLIGIBLE',\n",
-       "   'blocked': False},\n",
+       "   'probability_score': 0.083984375,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.029296875},\n",
        "  {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
        "   'probability_label': 'NEGLIGIBLE',\n",
-       "   'blocked': False}],\n",
-       " 'citation_metadata': None,\n",
+       "   'probability_score': 0.054931640625,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.03466796875}],\n",
        " 'usage_metadata': {'prompt_token_count': 10,\n",
-       "  'candidates_token_count': 30,\n",
-       "  'total_token_count': 40}}"
+       "  'candidates_token_count': 193,\n",
+       "  'total_token_count': 203,\n",
+       "  'prompt_tokens_details': [{'modality': 1, 'token_count': 10}],\n",
+       "  'candidates_tokens_details': [{'modality': 1, 'token_count': 193}],\n",
+       "  'cached_content_token_count': 0,\n",
+       "  'cache_tokens_details': []},\n",
+       " 'finish_reason': 'STOP',\n",
+       " 'avg_logprobs': -0.5702065976790196,\n",
+       " 'model_name': 'gemini-1.5-flash-001'}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,8 +154,8 @@
    "source": [
     "from langchain_google_vertexai import ChatVertexAI\n",
     "\n",
-    "llm = ChatVertexAI(model=\"gemini-pro\")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatVertexAI(model=\"gemini-1.5-flash-001\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   },
@@ -141,27 +169,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "id": "1e4ac668-4c6a-48ad-9a6f-7b291477b45d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'model_id': 'anthropic.claude-v2',\n",
-       " 'usage': {'prompt_tokens': 19, 'completion_tokens': 371, 'total_tokens': 390}}"
+       "{'ResponseMetadata': {'RequestId': 'ea0ac2ad-3ad5-4a49-9647-274a0c73ac31',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'date': 'Sat, 22 Mar 2025 11:27:46 GMT',\n",
+       "   'content-type': 'application/json',\n",
+       "   'content-length': '1660',\n",
+       "   'connection': 'keep-alive',\n",
+       "   'x-amzn-requestid': 'ea0ac2ad-3ad5-4a49-9647-274a0c73ac31'},\n",
+       "  'RetryAttempts': 0},\n",
+       " 'stopReason': 'end_turn',\n",
+       " 'metrics': {'latencyMs': [11044]}}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_aws import ChatBedrock\n",
+    "from langchain_aws import ChatBedrockConverse\n",
     "\n",
-    "llm = ChatBedrock(model_id=\"anthropic.claude-v2\")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatBedrockConverse(model=\"anthropic.claude-3-sonnet-20240229-v1:0\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   },
@@ -175,21 +211,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "deb41321-52d0-4795-a40c-4a811a13d7b0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'token_usage': {'prompt_tokens': 19,\n",
-       "  'total_tokens': 141,\n",
-       "  'completion_tokens': 122},\n",
-       " 'model': 'mistral-small',\n",
+       "{'token_usage': {'prompt_tokens': 13,\n",
+       "  'total_tokens': 219,\n",
+       "  'completion_tokens': 206},\n",
+       " 'model_name': 'mistral-small-latest',\n",
+       " 'model': 'mistral-small-latest',\n",
        " 'finish_reason': 'stop'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +234,7 @@
    "source": [
     "from langchain_mistralai import ChatMistralAI\n",
     "\n",
-    "llm = ChatMistralAI()\n",
+    "llm = ChatMistralAI(model=\"mistral-small-latest\")\n",
     "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
     "msg.response_metadata"
    ]
@@ -212,27 +249,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "744e14ec-ff50-4642-9893-ff7bdf8927ff",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'token_usage': {'completion_time': 0.243,\n",
-       "  'completion_tokens': 132,\n",
-       "  'prompt_time': 0.022,\n",
-       "  'prompt_tokens': 22,\n",
-       "  'queue_time': None,\n",
-       "  'total_time': 0.265,\n",
-       "  'total_tokens': 154},\n",
-       " 'model_name': 'mixtral-8x7b-32768',\n",
-       " 'system_fingerprint': 'fp_7b44c65f25',\n",
+       "{'token_usage': {'completion_tokens': 184,\n",
+       "  'prompt_tokens': 45,\n",
+       "  'total_tokens': 229,\n",
+       "  'completion_time': 0.245333333,\n",
+       "  'prompt_time': 0.002262803,\n",
+       "  'queue_time': 0.19315161,\n",
+       "  'total_time': 0.247596136},\n",
+       " 'model_name': 'llama-3.1-8b-instant',\n",
+       " 'system_fingerprint': 'fp_a56f6eea01',\n",
        " 'finish_reason': 'stop',\n",
        " 'logprobs': None}"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,53 +277,8 @@
    "source": [
     "from langchain_groq import ChatGroq\n",
     "\n",
-    "llm = ChatGroq()\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
-    "msg.response_metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7cdeec00-8a8f-422a-8819-47c646578b65",
-   "metadata": {},
-   "source": [
-    "## TogetherAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a984118e-a731-4864-bcea-7dc6c6b3d139",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'token_usage': {'completion_tokens': 208,\n",
-       "  'prompt_tokens': 20,\n",
-       "  'total_tokens': 228},\n",
-       " 'model_name': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
-       " 'system_fingerprint': None,\n",
-       " 'finish_reason': 'eos',\n",
-       " 'logprobs': None}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(\n",
-    "    base_url=\"https://api.together.xyz/v1\",\n",
-    "    api_key=os.environ[\"TOGETHER_API_KEY\"],\n",
-    "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
-    ")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatGroq(model=\"llama-3.1-8b-instant\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   },
@@ -300,23 +292,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "id": "6ae32a93-26db-41bb-95c2-38ddd5085fbe",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'token_usage': {'prompt_tokens': 19,\n",
-       "  'total_tokens': 219,\n",
-       "  'completion_tokens': 200},\n",
-       " 'model_name': 'accounts/fireworks/models/mixtral-8x7b-instruct',\n",
+       "{'token_usage': {'prompt_tokens': 25,\n",
+       "  'total_tokens': 352,\n",
+       "  'completion_tokens': 327},\n",
+       " 'model_name': 'accounts/fireworks/models/llama-v3p1-70b-instruct',\n",
        " 'system_fingerprint': '',\n",
-       " 'finish_reason': 'length',\n",
+       " 'finish_reason': 'stop',\n",
        " 'logprobs': None}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -324,17 +316,17 @@
    "source": [
     "from langchain_fireworks import ChatFireworks\n",
     "\n",
-    "llm = ChatFireworks(model=\"accounts/fireworks/models/mixtral-8x7b-instruct\")\n",
-    "msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
+    "llm = ChatFireworks(model=\"accounts/fireworks/models/llama-v3p1-70b-instruct\")\n",
+    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -346,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/split_html.ipynb

@@ -599,7 +599,7 @@
     "\n",
     "The `HTMLSemanticPreservingSplitter` is designed to split HTML content into manageable chunks while preserving the semantic structure of important elements like tables, lists, and other HTML components. This ensures that such elements are not split across chunks, causing loss of contextual relevancy such as table headers, list headers etc.\n",
     "\n",
-    "This splitter is designed at its heart, to create contextually relevant chunks. General Recursive splitting with `HTMLHeaderTextSplitter` can cause tables, lists and other structered elements to be split in the middle, losing signifcant context and creating bad chunks.\n",
+    "This splitter is designed at its heart, to create contextually relevant chunks. General Recursive splitting with `HTMLHeaderTextSplitter` can cause tables, lists and other structured elements to be split in the middle, losing significant context and creating bad chunks.\n",
     "\n",
     "The `HTMLSemanticPreservingSplitter` is essential for splitting HTML content that includes structured elements like tables and lists, especially when it's critical to preserve these elements intact. Additionally, its ability to define custom handlers for specific HTML tags makes it a versatile tool for processing complex HTML documents.\n",
     "\n",
@@ -832,7 +832,7 @@
     "#### Explanation\n",
     "In this example, we defined a custom handler for `iframe` tags that converts them into Markdown-like links. When the splitter processes the HTML content, it uses this custom handler to transform the `iframe` tags while preserving other elements like tables and lists. The resulting `Document` objects show how the iframe is handled according to the custom logic you provided.\n",
     "\n",
-    "**Important**: When presvering items such as links, you should be mindful not to include `.` in your seperators, or leave seperators blank. `RecursiveCharacterTextSplitter` splits on full stop, which will cut links in half. Ensure you provide a seperator list with `. `  instead."
+    "**Important**: When presvering items such as links, you should be mindful not to include `.` in your separators, or leave separators blank. `RecursiveCharacterTextSplitter` splits on full stop, which will cut links in half. Ensure you provide a separator list with `. `  instead."
    ]
   },
   {

docs/docs/how_to/tools_human.ipynb

@@ -161,7 +161,7 @@
    "source": [
     "## Adding human approval\n",
     "\n",
-    "Let's add a step in the chain that will ask a person to approve or reject the tall call request.\n",
+    "Let's add a step in the chain that will ask a person to approve or reject the tool call request.\n",
     "\n",
     "On rejection, the step will raise an exception which will stop execution of the rest of the chain."
    ]