core[patch]: Release 0.3.2 (#26686)

Complementary PR to master for #26643.

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -96,25 +96,21 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us. 
+        Please share your system info with us. Do NOT skip this step and please don't trim
+        the output. Most users don't include enough information here and it makes it harder
+        for us to help you.
         
-        "pip freeze | grep langchain" 
-        platform (windows / linux / mac)
-        python version
-        
-        OR if you're on a recent version of langchain-core you can paste the output of:
+        Run the following command in your terminal and paste the output here:
         
         python -m langchain_core.sys_info
+        
+        or if you have an existing python interpreter running:
+        
+        from langchain_core import sys_info
+        sys_info.print_sys_info()
+        
+        alternatively, put the entire output of `pip freeze` here.
       placeholder: |
-        "pip freeze | grep langchain"
-        platform
-        python version
-        
-        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
-        
         python -m langchain_core.sys_info
-        
-        These will only surface LangChain packages, don't forget to include any other relevant
-        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true

.github/scripts/get_min_versions.py

@@ -21,7 +21,14 @@ MIN_VERSION_LIBS = [
     "SQLAlchemy",
 ]
 
-SKIP_IF_PULL_REQUEST = ["langchain-core"]
+# some libs only get checked on release because of simultaneous changes in
+# multiple libs
+SKIP_IF_PULL_REQUEST = [
+    "langchain-core",
+    "langchain-text-splitters",
+    "langchain",
+    "langchain-community",
+]
 
 
 def get_min_version(version: str) -> str:
@@ -70,7 +77,7 @@ def get_min_version_from_toml(
     for lib in set(MIN_VERSION_LIBS + (include or [])):
         if versions_for == "pull_request" and lib in SKIP_IF_PULL_REQUEST:
             # some libs only get checked on release because of simultaneous
-            # changes
+            # changes in multiple libs
             continue
         # Check if the lib is present in the dependencies
         if lib in dependencies:
@@ -88,7 +95,6 @@ def get_min_version_from_toml(
                     if check_python_version(python_version, vs["python"])
                 ][0]["version"]
 
-
             # Use parse_version to get the minimum supported version from version_string
             min_version = get_min_version(version_string)
 

.github/workflows/_release.yml

@@ -85,7 +85,7 @@ jobs:
           path: langchain
           sparse-checkout: | # this only grabs files for relevant dir
             ${{ inputs.working-directory }}
-          ref: master # this scopes to just master branch
+          ref: ${{ github.ref }} # this scopes to just ref'd branch
           fetch-depth: 0 # this fetches entire commit history
       - name: Check Tags
         id: check-tags

.github/workflows/_test.yml

@@ -58,7 +58,7 @@ jobs:
         env:
           MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
         run: |
-          poetry run pip install --force-reinstall $MIN_VERSIONS --editable .
+          poetry run pip install $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 

docs/api_reference/templates/runnable_pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema, predict, apredict, predict_messages, apredict_messages, generate, generate_prompt, agenerate, agenerate_prompt, call_as_llm
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 

docs/docs/how_to/add_scores_retriever.ipynb

@@ -206,7 +206,7 @@
     "    ) -> List[Document]:\n",
     "        \"\"\"Get docs, adding score information.\"\"\"\n",
     "        docs, scores = zip(\n",
-    "            *vectorstore.similarity_search_with_score(query, **search_kwargs)\n",
+    "            *self.vectorstore.similarity_search_with_score(query, **search_kwargs)\n",
     "        )\n",
     "        for doc, score in zip(docs, scores):\n",
     "            doc.metadata[\"score\"] = score\n",

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -15,43 +15,15 @@
     "\n",
     "Make sure you have the integration packages installed for any model providers you want to support. E.g. you should have `langchain-openai` installed to init an OpenAI model.\n",
     "\n",
-    ":::\n",
-    "\n",
-    ":::info Requires ``langchain >= 0.2.8``\n",
-    "\n",
-    "This functionality was added in ``langchain-core == 0.2.8``. Please make sure your package is up to date.\n",
-    "\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "165b0de6-9ae3-4e3d-aa98-4fc8a97c4a06",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-09-10T20:22:32.858670Z",
-     "iopub.status.busy": "2024-09-10T20:22:32.858278Z",
-     "iopub.status.idle": "2024-09-10T20:22:33.009452Z",
-     "shell.execute_reply": "2024-09-10T20:22:33.007022Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zsh:1: 0.2.8 not found\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai"
    ]

docs/docs/integrations/document_loaders/amazon_textract.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "This sample demonstrates the use of `Amazon Textract` in combination with LangChain as a DocumentLoader.\n",
     "\n",
-    "`Textract` supports`PDF`, `TIF`F, `PNG` and `JPEG` format.\n",
+    "`Textract` supports`PDF`, `TIFF`, `PNG` and `JPEG` format.\n",
     "\n",
     "`Textract` supports these [document sizes, languages and characters](https://docs.aws.amazon.com/textract/latest/dg/limits-document.html)."
    ]

docs/docs/integrations/document_loaders/google_speech_to_text.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Google Speech-to-Text Audio Transcripts\n",
     "\n",
-    "The `GoogleSpeechToTextLoader` allows to transcribe audio files with the [Google Cloud Speech-to-Text API](https://cloud.google.com/speech-to-text) and loads the transcribed text into documents.\n",
+    "The `SpeechToTextLoader` allows to transcribe audio files with the [Google Cloud Speech-to-Text API](https://cloud.google.com/speech-to-text) and loads the transcribed text into documents.\n",
     "\n",
     "To use it, you should have the `google-cloud-speech` python package installed, and a Google Cloud project with the [Speech-to-Text API enabled](https://cloud.google.com/speech-to-text/v2/docs/transcribe-client-libraries#before_you_begin).\n",
     "\n",
@@ -41,7 +41,7 @@
    "source": [
     "## Example\n",
     "\n",
-    "The `GoogleSpeechToTextLoader` must include the `project_id` and `file_path` arguments. Audio files can be specified as a Google Cloud Storage URI (`gs://...`) or a local file path.\n",
+    "The `SpeechToTextLoader` must include the `project_id` and `file_path` arguments. Audio files can be specified as a Google Cloud Storage URI (`gs://...`) or a local file path.\n",
     "\n",
     "Only synchronous requests are supported by the loader, which has a [limit of 60 seconds or 10MB](https://cloud.google.com/speech-to-text/v2/docs/sync-recognize#:~:text=60%20seconds%20and/or%2010%20MB) per audio file."
    ]
@@ -52,13 +52,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_google_community import GoogleSpeechToTextLoader\n",
+    "from langchain_google_community import SpeechToTextLoader\n",
     "\n",
     "project_id = \"<PROJECT_ID>\"\n",
     "file_path = \"gs://cloud-samples-data/speech/audio.flac\"\n",
     "# or a local file path: file_path = \"./audio.wav\"\n",
     "\n",
-    "loader = GoogleSpeechToTextLoader(project_id=project_id, file_path=file_path)\n",
+    "loader = SpeechToTextLoader(project_id=project_id, file_path=file_path)\n",
     "\n",
     "docs = loader.load()"
    ]
@@ -152,7 +152,7 @@
     "    RecognitionConfig,\n",
     "    RecognitionFeatures,\n",
     ")\n",
-    "from langchain_google_community import GoogleSpeechToTextLoader\n",
+    "from langchain_google_community import SpeechToTextLoader\n",
     "\n",
     "project_id = \"<PROJECT_ID>\"\n",
     "location = \"global\"\n",
@@ -171,7 +171,7 @@
     "    ),\n",
     ")\n",
     "\n",
-    "loader = GoogleSpeechToTextLoader(\n",
+    "loader = SpeechToTextLoader(\n",
     "    project_id=project_id,\n",
     "    location=location,\n",
     "    recognizer_id=recognizer_id,\n",

docs/docs/integrations/document_loaders/unstructured_file.ipynb

@@ -16,7 +16,7 @@
     "\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredLoader](https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html) | [langchain_community](https://python.langchain.com/api_reference/unstructured/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [UnstructuredLoader](https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html) | [langchain_unstructured](https://python.langchain.com/api_reference/unstructured/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -519,6 +519,47 @@
     "print(\"Length of text in the document:\", len(docs[0].page_content))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3ec3c22d-02cd-498b-921f-b839d1404f32",
+   "metadata": {},
+   "source": [
+    "## Loading web pages\n",
+    "\n",
+    "`UnstructuredLoader` accepts a `web_url` kwarg when run locally that populates the `url` parameter of the underlying Unstructured [partition](https://docs.unstructured.io/open-source/core-functionality/partitioning). This allows for the parsing of remotely hosted documents, such as HTML web pages.\n",
+    "\n",
+    "Example usage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bf9a8546-659d-4861-bff2-fdf1ad93ac65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='Example Domain' metadata={'category_depth': 0, 'languages': ['eng'], 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'Title', 'element_id': 'fdaa78d856f9d143aeeed85bf23f58f8'}\n",
+      "\n",
+      "page_content='This domain is for use in illustrative examples in documents. You may use this domain in literature without prior coordination or asking for permission.' metadata={'languages': ['eng'], 'parent_id': 'fdaa78d856f9d143aeeed85bf23f58f8', 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'NarrativeText', 'element_id': '3652b8458b0688639f973fe36253c992'}\n",
+      "\n",
+      "page_content='More information...' metadata={'category_depth': 0, 'link_texts': ['More information...'], 'link_urls': ['https://www.iana.org/domains/example'], 'languages': ['eng'], 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'Title', 'element_id': '793ab98565d6f6d6f3a6d614e3ace2a9'}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_unstructured import UnstructuredLoader\n",
+    "\n",
+    "loader = UnstructuredLoader(web_url=\"https://www.example.com\")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "for doc in docs:\n",
+    "    print(f\"{doc}\\n\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ce01aa40",
@@ -546,7 +587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/sambanova.ipynb

@@ -6,129 +6,11 @@
    "source": [
     "# SambaNova\n",
     "\n",
-    "**[SambaNova](https://sambanova.ai/)'s** [Sambaverse](https://sambaverse.sambanova.ai/) and [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) are platforms for running your own open-source models\n",
+    "**[SambaNova](https://sambanova.ai/)'s** [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
     "\n",
     "This example goes over how to use LangChain to interact with SambaNova models"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sambaverse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Sambaverse** allows you to interact with multiple open-source models. You can view the list of available models and interact with them in the [playground](https://sambaverse.sambanova.ai/playground).\n",
-    " **Please note that Sambaverse's free offering is performance-limited.** Companies that are ready to evaluate the production tokens-per-second performance, volume throughput, and 10x lower total cost of ownership (TCO) of SambaNova should [contact us](https://sambaverse.sambanova.ai/contact-us) for a non-limited evaluation instance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An API key is required to access Sambaverse models. To get a key, create an account at [sambaverse.sambanova.ai](https://sambaverse.sambanova.ai/)\n",
-    "\n",
-    "The [sseclient-py](https://pypi.org/project/sseclient-py/) package is required to run streaming predictions "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet sseclient-py==1.8.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Register your API key as an environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "sambaverse_api_key = \"<Your sambaverse API key>\"\n",
-    "\n",
-    "# Set the environment variables\n",
-    "os.environ[\"SAMBAVERSE_API_KEY\"] = sambaverse_api_key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call Sambaverse models directly from LangChain!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms.sambanova import Sambaverse\n",
-    "\n",
-    "llm = Sambaverse(\n",
-    "    sambaverse_model_name=\"Meta/llama-2-7b-chat-hf\",\n",
-    "    streaming=False,\n",
-    "    model_kwargs={\n",
-    "        \"do_sample\": True,\n",
-    "        \"max_tokens_to_generate\": 1000,\n",
-    "        \"temperature\": 0.01,\n",
-    "        \"select_expert\": \"llama-2-7b-chat-hf\",\n",
-    "        \"process_prompt\": False,\n",
-    "        # \"stop_sequences\": '\\\"sequence1\\\",\\\"sequence2\\\"',\n",
-    "        # \"repetition_penalty\":  1.0,\n",
-    "        # \"top_k\": 50,\n",
-    "        # \"top_p\": 1.0\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "print(llm.invoke(\"Why should I use open source models?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Streaming response\n",
-    "\n",
-    "from langchain_community.llms.sambanova import Sambaverse\n",
-    "\n",
-    "llm = Sambaverse(\n",
-    "    sambaverse_model_name=\"Meta/llama-2-7b-chat-hf\",\n",
-    "    streaming=True,\n",
-    "    model_kwargs={\n",
-    "        \"do_sample\": True,\n",
-    "        \"max_tokens_to_generate\": 1000,\n",
-    "        \"temperature\": 0.01,\n",
-    "        \"select_expert\": \"llama-2-7b-chat-hf\",\n",
-    "        \"process_prompt\": False,\n",
-    "        # \"stop_sequences\": '\\\"sequence1\\\",\\\"sequence2\\\"',\n",
-    "        # \"repetition_penalty\":  1.0,\n",
-    "        # \"top_k\": 50,\n",
-    "        # \"top_p\": 1.0\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "for chunk in llm.stream(\"Why should I use open source models?\"):\n",
-    "    print(chunk, end=\"\", flush=True)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/providers/mlflow.mdx

@@ -1,12 +1,12 @@
-# MLflow Deployments for LLMs
+# MLflow AI Gateway for LLMs
 
->[The MLflow Deployments for LLMs](https://www.mlflow.org/docs/latest/llms/deployments/index.html) is a powerful tool designed to streamline the usage and management of various large
+>[The MLflow AI Gateway for LLMs](https://www.mlflow.org/docs/latest/llms/deployments/index.html) is a powerful tool designed to streamline the usage and management of various large
 > language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface
 > that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests.
 
 ## Installation and Setup
 
-Install `mlflow` with MLflow Deployments dependencies:
+Install `mlflow` with MLflow GenAI dependencies:
 
 ```sh
 pip install 'mlflow[genai]'
@@ -39,10 +39,10 @@ endpoints:
         openai_api_key: $OPENAI_API_KEY
 ```
 
-Start the deployments server:
+Start the gateway server:
 
 ```sh
-mlflow deployments start-server --config-path /path/to/config.yaml
+mlflow gateway start --config-path /path/to/config.yaml
 ```
 
 ## Example provided by `MLflow`

docs/docs/integrations/providers/mlflow_ai_gateway.mdx

@@ -1,160 +0,0 @@
-# MLflow AI Gateway
-
-:::warning
-
-MLflow AI Gateway has been deprecated. Please use [MLflow Deployments for LLMs](/docs/integrations/providers/mlflow/) instead.
-
-:::
-
->[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/index.html) service is a powerful tool designed to streamline the usage and management of various large 
-> language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface 
-> that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests.
-
-## Installation and Setup
-
-Install `mlflow` with MLflow AI Gateway dependencies:
-
-```sh
-pip install 'mlflow[gateway]'
-```
-
-Set the OpenAI API key as an environment variable:
-
-```sh
-export OPENAI_API_KEY=...
-```
-
-Create a configuration file:
-
-```yaml
-routes:
-  - name: completions
-    route_type: llm/v1/completions
-    model:
-      provider: openai
-      name: text-davinci-003
-      config:
-        openai_api_key: $OPENAI_API_KEY
-
-  - name: embeddings
-    route_type: llm/v1/embeddings
-    model:
-      provider: openai
-      name: text-embedding-ada-002
-      config:
-        openai_api_key: $OPENAI_API_KEY
-```
-
-Start the Gateway server:
-
-```sh
-mlflow gateway start --config-path /path/to/config.yaml
-```
-
-## Example provided by `MLflow`
-
->The `mlflow.langchain` module provides an API for logging and loading `LangChain` models. 
-> This module exports multivariate LangChain models in the langchain flavor and univariate LangChain 
-> models in the pyfunc flavor.
- 
-See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain.html?highlight=langchain#module-mlflow.langchain).
-
-
-
-## Completions Example
-
-```python
-import mlflow
-from langchain.chains import LLMChain, PromptTemplate
-from langchain_community.llms import MlflowAIGateway
-
-gateway = MlflowAIGateway(
-    gateway_uri="http://127.0.0.1:5000",
-    route="completions",
-    params={
-        "temperature": 0.0,
-        "top_p": 0.1,
-    },
-)
-
-llm_chain = LLMChain(
-    llm=gateway,
-    prompt=PromptTemplate(
-        input_variables=["adjective"],
-        template="Tell me a {adjective} joke",
-    ),
-)
-result = llm_chain.run(adjective="funny")
-print(result)
-
-with mlflow.start_run():
-    model_info = mlflow.langchain.log_model(chain, "model")
-
-model = mlflow.pyfunc.load_model(model_info.model_uri)
-print(model.predict([{"adjective": "funny"}]))
-```
-
-## Embeddings Example
-
-```python
-from langchain_community.embeddings import MlflowAIGatewayEmbeddings
-
-embeddings = MlflowAIGatewayEmbeddings(
-    gateway_uri="http://127.0.0.1:5000",
-    route="embeddings",
-)
-
-print(embeddings.embed_query("hello"))
-print(embeddings.embed_documents(["hello"]))
-```
-
-## Chat Example
-
-```python
-from langchain_community.chat_models import ChatMLflowAIGateway
-from langchain_core.messages import HumanMessage, SystemMessage
-
-chat = ChatMLflowAIGateway(
-    gateway_uri="http://127.0.0.1:5000",
-    route="chat",
-    params={
-        "temperature": 0.1
-    }
-)
-
-messages = [
-    SystemMessage(
-        content="You are a helpful assistant that translates English to French."
-    ),
-    HumanMessage(
-        content="Translate this sentence from English to French: I love programming."
-    ),
-]
-print(chat(messages))
-```
-
-## Databricks MLflow AI Gateway
-
-Databricks MLflow AI Gateway is in private preview.
-Please contact a Databricks representative to enroll in the preview.
-
-```python
-from langchain.chains import LLMChain
-from langchain_core.prompts import PromptTemplate
-from langchain_community.llms import MlflowAIGateway
-
-gateway = MlflowAIGateway(
-    gateway_uri="databricks",
-    route="completions",
-)
-
-llm_chain = LLMChain(
-    llm=gateway,
-    prompt=PromptTemplate(
-        input_variables=["adjective"],
-        template="Tell me a {adjective} joke",
-    ),
-)
-result = llm_chain.run(adjective="funny")
-print(result)
-```

docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb

@@ -400,18 +400,29 @@
     "def hybrid_query(search_query: str) -> Dict:\n",
     "    vector = embeddings.embed_query(search_query)  # same embeddings as for indexing\n",
     "    return {\n",
-    "        \"query\": {\n",
-    "            \"match\": {\n",
-    "                text_field: search_query,\n",
-    "            },\n",
-    "        },\n",
-    "        \"knn\": {\n",
-    "            \"field\": dense_vector_field,\n",
-    "            \"query_vector\": vector,\n",
-    "            \"k\": 5,\n",
-    "            \"num_candidates\": 10,\n",
-    "        },\n",
-    "        \"rank\": {\"rrf\": {}},\n",
+    "        \"retriever\": {\n",
+    "            \"rrf\": {\n",
+    "                \"retrievers\": [\n",
+    "                    {\n",
+    "                        \"standard\": {\n",
+    "                            \"query\": {\n",
+    "                                \"match\": {\n",
+    "                                    text_field: search_query,\n",
+    "                                }\n",
+    "                            }\n",
+    "                        }\n",
+    "                    },\n",
+    "                    {\n",
+    "                        \"knn\": {\n",
+    "                            \"field\": dense_vector_field,\n",
+    "                            \"query_vector\": vector,\n",
+    "                            \"k\": 5,\n",
+    "                            \"num_candidates\": 10,\n",
+    "                        }\n",
+    "                    },\n",
+    "                ]\n",
+    "            }\n",
+    "        }\n",
     "    }\n",
     "\n",
     "\n",

docs/docs/integrations/vectorstores/chroma.ipynb

@@ -99,7 +99,7 @@
     "vector_store = Chroma(\n",
     "    collection_name=\"example_collection\",\n",
     "    embedding_function=embeddings,\n",
-    "    persist_directory=\"./chroma_langchain_db\",  # Where to save data locally, remove if not neccesary\n",
+    "    persist_directory=\"./chroma_langchain_db\",  # Where to save data locally, remove if not necessary\n",
     ")"
    ]
   },
@@ -179,7 +179,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     "    id=1,\n",
     ")\n",
@@ -273,7 +273,7 @@
    "outputs": [],
    "source": [
     "updated_document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and fried eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and fried eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     "    id=1,\n",
     ")\n",
@@ -287,7 +287,7 @@
     "vector_store.update_document(document_id=uuids[0], document=updated_document_1)\n",
     "# You can also update multiple documents at once\n",
     "vector_store.update_documents(\n",
-    "    ids=uuids[:2], documents=[updated_document_1, updated_document_1]\n",
+    "    ids=uuids[:2], documents=[updated_document_1, updated_document_2]\n",
     ")"
    ]
   },

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -380,7 +380,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html"
+    "For detailed documentation of all `Clickhouse` features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html"
    ]
   }
  ],

docs/docs/tutorials/rag.ipynb

@@ -90,7 +90,7 @@
    "source": [
     "  </TabItem>\n",
     "  <TabItem value=\"conda\" label=\"Conda\">\n",
-    "    <CodeBlock language=\"bash\">conda install langchain langchain_community langchain_chroma -c conda-forge</CodeBlock>\n",
+    "    <CodeBlock language=\"bash\">conda install langchain langchain-community langchain-chroma -c conda-forge</CodeBlock>\n",
     "  </TabItem>\n",
     "</Tabs>\n",
     "\n",

docs/docs/versions/v0_3/index.mdx

@@ -1,6 +1,6 @@
 # LangChain v0.3
 
-*Last updated: 09.13.24*
+*Last updated: 09.16.24*
 
 ## What's changed
 
@@ -23,7 +23,7 @@ The following features have been added during the development of 0.2.x:
 
 ## How to update your code
 
-If you're using `langchain` / `langchain-community` / `langchain-core` 0.0 or 0.1, we recommend that you first [upgrade to 0.2](https://python.langchain.com/v0.2/docs/versions/v0_2/). The `langchain-cli` will help you to migrate many imports automatically.
+If you're using `langchain` / `langchain-community` / `langchain-core` 0.0 or 0.1, we recommend that you first [upgrade to 0.2](https://python.langchain.com/v0.2/docs/versions/v0_2/).
 
 If you're using `langgraph`, upgrade to `langgraph>=0.2.20,<0.3`. This will work with either 0.2 or 0.3 versions of all the base packages.
 
@@ -31,22 +31,27 @@ Here is a complete list of all packages that have been released and what we reco
 Any package that now requires `langchain-core` 0.3 had a minor version bump.
 Any package that is now compatible with both `langchain-core` 0.2 and 0.3 had a patch version bump.
 
+You can use the `langchain-cli` to update deprecated imports automatically.
+The CLI will handle updating deprecated imports that were introduced in LangChain 0.0.x and LangChain 0.1, as
+well as updating the `langchain_core.pydantic_v1` and `langchain.pydantic_v1` imports.
+
+
 ### Base packages
 
-| Package                                | Latest  | Recommended constraint     |
-| -------------------------------------- | ------- | -------------------------- |
-| langchain                              | 0.3.0   | >=0.3,<0.4                 |
-| langchain-community                    | 0.3.0   | >=0.3,<0.4                 |
-| langchain-text-splitters               | 0.3.0   | >=0.3,<0.4                 |
-| langchain-core                         | 0.3.0   | >=0.3,<0.4                 |
-| langchain-experimental                 | 0.3.0   | >=0.3,<0.4                 |
+| Package                  | Latest | Recommended constraint |
+|--------------------------|--------|------------------------|
+| langchain                | 0.3.0  | >=0.3,<0.4             |
+| langchain-community      | 0.3.0  | >=0.3,<0.4             |
+| langchain-text-splitters | 0.3.0  | >=0.3,<0.4             |
+| langchain-core           | 0.3.0  | >=0.3,<0.4             |
+| langchain-experimental   | 0.3.0  | >=0.3,<0.4             |
 
 ### Downstream packages
 
-| Package                                | Latest  | Recommended constraint     |
-| -------------------------------------- | ------- | -------------------------- |
-| langgraph                              | 0.2.20  | >=0.2.20,<0.3              |
-| langserve                              | 0.3.0   | >=0.3,<0.4                 |
+| Package   | Latest | Recommended constraint |
+|-----------|--------|------------------------|
+| langgraph | 0.2.20 | >=0.2.20,<0.3          |
+| langserve | 0.3.0  | >=0.3,<0.4             |
 
 ### Integration packages
 
@@ -59,7 +64,7 @@ Any package that is now compatible with both `langchain-core` 0.2 and 0.3 had a
 | langchain-azure-dynamic-sessions       | 0.2.0   | >=0.2,<0.3                 |
 | langchain-box                          | 0.2.0   | >=0.2,<0.3                 |
 | langchain-chroma                       | 0.1.4   | >=0.1.4,<0.2               |
-| langchain-cohere                       | 0.2.0   | >=0.2,<0.3                 |
+| langchain-cohere                       | 0.3.0   | >=0.3,<0.4                 |
 | langchain-elasticsearch                | 0.3.0   | >=0.3,<0.4                 |
 | langchain-exa                          | 0.2.0   | >=0.2,<0.3                 |
 | langchain-fireworks                    | 0.2.0   | >=0.2,<0.3                 |
@@ -68,6 +73,7 @@ Any package that is now compatible with both `langchain-core` 0.2 and 0.3 had a
 | langchain-google-genai                 | 2.0.0   | >=2,<3                     |
 | langchain-google-vertexai              | 2.0.0   | >=2,<3                     |
 | langchain-huggingface                  | 0.1.0   | >=0.1,<0.2                 |
+| langchain-ibm                          | 0.2.0   | >=0.2,<0.3                 |
 | langchain-milvus                       | 0.1.6   | >=0.1.6,<0.2               |
 | langchain-mistralai                    | 0.2.0   | >=0.2,<0.3                 |
 | langchain-mongodb                      | 0.2.0   | >=0.2,<0.3                 |
@@ -77,12 +83,14 @@ Any package that is now compatible with both `langchain-core` 0.2 and 0.3 had a
 | langchain-pinecone                     | 0.2.0   | >=0.2,<0.3                 |
 | langchain-postgres                     | 0.0.13  | >=0.0.13,<0.1              |
 | langchain-prompty                      | 0.1.0   | >=0.1,<0.2                 |
+| langchain-qdrant                       | 0.1.4   | >=0.1.4,<0.2               |
 | langchain-redis                        | 0.1.0   | >=0.1,<0.2                 |
-| langchain-qdrant                       | 0.2.0   | >=0.2,<0.3                 |
+| langchain-sema4                        | 0.2.0   | >=0.2,<0.3                 |
 | langchain-together                     | 0.2.0   | >=0.2,<0.3                 |
 | langchain-unstructured                 | 0.1.4   | >=0.1.4,<0.2               |
+| langchain-upstage                      | 0.3.0   | >=0.3,<0.4                 |
 | langchain-voyageai                     | 0.2.0   | >=0.2,<0.3                 |
-| langchain-weaviate                     | 0.1.0   | >=0.1,<0.2                 |
+| langchain-weaviate                     | 0.0.3   | >=0.0.3,<0.1               |
 
 Once you've updated to recent versions of the packages, you may need to address the following issues stemming from the internal switch from Pydantic v1 to Pydantic v2:
 
@@ -185,6 +193,8 @@ CustomTool(
 When sub-classing from LangChain models, users may need to add relevant imports
 to the file and rebuild the model.
 
+You can read more about `model_rebuild` [here](https://docs.pydantic.dev/latest/concepts/models/#rebuilding-model-schema).
+
 ```python
 from langchain_core.output_parsers import BaseOutputParser
 
@@ -205,3 +215,57 @@ class FooParser(BaseOutputParser):
 
 FooParser.model_rebuild()
 ```
+
+## Migrate using langchain-cli
+
+The `langchain-cli` can help update deprecated LangChain imports in your code automatically.
+
+Please note that the `langchain-cli` only handles deprecated LangChain imports and cannot
+help to upgrade your code from pydantic 1 to pydantic 2.
+
+For help with the Pydantic 1 to 2 migration itself please refer to the [Pydantic Migration Guidelines](https://docs.pydantic.dev/latest/migration/).
+
+As of 0.0.31, the `langchain-cli` relies on [gritql](https://about.grit.io/) for applying code mods.
+
+### Installation
+
+```bash
+pip install -U langchain-cli
+langchain-cli --version # <-- Make sure the version is at least 0.0.31
+```
+
+### Usage
+
+Given that the migration script is not perfect, you should make sure you have a backup of your code first (e.g., using version control like `git`).
+
+The `langchain-cli` will handle the `langchain_core.pydantic_v1` deprecation introduced in LangChain 0.3 as well
+as older deprecations (e.g.,`from langchain.chat_models import ChatOpenAI` which should be `from langchain_openai import ChatOpenAI`),
+
+You will need to run the migration script **twice** as it only applies one import replacement per run.
+
+For example, say that your code is still using the old import `from langchain.chat_models import ChatOpenAI`:
+
+After the first run, you’ll get: `from langchain_community.chat_models import ChatOpenAI`
+After the second run, you’ll get: `from langchain_openai import ChatOpenAI`
+
+```bash
+# Run a first time
+# Will replace from langchain.chat_models import ChatOpenAI
+langchain-cli migrate --help [path to code] # Help
+langchain-cli migrate [path to code] # Apply
+
+# Run a second time to apply more import replacements
+langchain-cli migrate --diff [path to code] # Preview
+langchain-cli migrate [path to code] # Apply
+```
+
+### Other options
+
+```bash
+# See help menu
+langchain-cli migrate --help
+# Preview Changes without applying
+langchain-cli migrate --diff [path to code]
+# Approve changes interactively
+langchain-cli migrate --interactive [path to code]
+```

docs/docusaurus.config.js

@@ -168,52 +168,43 @@ const config = {
             label: "Integrations",
           },
           {
-            type: "dropdown",
-            label: "API reference",
-            position: "left",
-            items: [
-              {
-                label: "Latest",
-                to: "https://python.langchain.com/api_reference/reference.html",
-              },
-              {
-                label: "Legacy",
-                href: "https://api.python.langchain.com/"
-              }
-            ]
+            label: "API Reference",
+            to: "https://python.langchain.com/api_reference/",
           },
           {
             type: "dropdown",
             label: "More",
             position: "left",
             items: [
-              {
-                type: "doc",
-                docId: "people",
-                label: "People",
-              },
               {
                 type: "doc",
                 docId: "contributing/index",
                 label: "Contributing",
               },
-              {
-                label: "Cookbooks",
-                href: "https://github.com/langchain-ai/langchain/blob/master/cookbook/README.md"
-              },
               {
                 type: "doc",
-                docId: "additional_resources/tutorials",
-                label: "3rd party tutorials"
+                docId: "people",
+                label: "People",
               },
               {
-                type: "doc",
-                docId: "additional_resources/youtube",
-                label: "YouTube"
+                type: 'html',
+                value: '<hr class="dropdown-separator" style="margin-top: 0.5rem; margin-bottom: 0.5rem">',
               },
               {
-                to: "/docs/additional_resources/arxiv_references",
-                label: "arXiv"
+                href: "https://docs.smith.langchain.com",
+                label: "LangSmith",
+              },
+              {
+                href: "https://langchain-ai.github.io/langgraph/",
+                label: "LangGraph",
+              },
+              {
+                href: "https://smith.langchain.com/hub",
+                label: "LangChain Hub",
+              },
+              {
+                href: "https://js.langchain.com",
+                label: "LangChain JS/TS",
               },
             ]
           },
@@ -237,30 +228,7 @@ const config = {
             ]
           },
           {
-            type: "dropdown",
-            label: "🦜️🔗",
-            position: "right",
-            items: [
-              {
-                href: "https://smith.langchain.com",
-                label: "LangSmith",
-              },
-              {
-                href: "https://docs.smith.langchain.com/",
-                label: "LangSmith Docs",
-              },
-              {
-                href: "https://smith.langchain.com/hub",
-                label: "LangChain Hub",
-              },
-              {
-                href: "https://js.langchain.com",
-                label: "JS/TS Docs",
-              },
-            ]
-          },
-          {
-            href: "https://chat.langchain.com",
+            to: "https://chat.langchain.com",
             label: "💬",
             position: "right",
           },

docs/src/css/custom.css

@@ -38,6 +38,9 @@
   --ifm-menu-link-padding-horizontal: 0.5rem;
   --ifm-menu-link-padding-vertical: 0.5rem;
   --doc-sidebar-width: 275px !important;
+
+  /* Code block syntax highlighting */
+  --docusaurus-highlighted-code-line-bg: rgb(176, 227, 199);
 }
 
 /* For readability concerns, you should choose a lighter palette in dark mode. */
@@ -49,6 +52,9 @@
   --ifm-color-primary-light: #29d5b0;
   --ifm-color-primary-lighter: #32d8b4;
   --ifm-color-primary-lightest: #4fddbf;
+
+  /* Code block syntax highlighting */
+  --docusaurus-highlighted-code-line-bg: rgb(14, 73, 60);
 }
 
 nav, h1, h2, h3, h4 {

docs/src/theme/FeatureTables.js

@@ -354,7 +354,7 @@ const FEATURE_TABLES = {
             },
             {
                 name: "Nomic",
-                link: "cohere",
+                link: "nomic",
                 package: "langchain-nomic",
                 apiLink: "https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html"
             },
@@ -886,7 +886,7 @@ const FEATURE_TABLES = {
                 apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
             },
             {
-                name: "UnstrucutredXMLLoader",
+                name: "UnstructuredXMLLoader",
                 link: "xml",
                 source: "XML files",
                 apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"

docs/vercel.json

@@ -26,6 +26,22 @@
     }
   ],
   "redirects": [
+    {
+      "source": "/v0.3/docs/:path(.*/?)*",
+      "destination": "/docs/:path*"
+    },
+    {
+      "source": "/docs/modules/agents/tools/custom_tools(/?)",
+      "destination": "/docs/how_to/custom_tools/"
+    },
+    {
+      "source": "/docs/expression_language(/?)",
+      "destination": "/docs/concepts/#langchain-expression-language-lcel"
+    },
+    {
+      "source": "/docs/expression_language/interface(/?)",
+      "destination": "/docs/concepts/#runnable-interface"
+    },
     {
       "source": "/docs/versions/overview(/?)",
       "destination": "/docs/versions/v0_2/overview/"
@@ -61,6 +77,10 @@
     {
       "source": "/v0.2/docs/templates/:path(.*/?)*",
       "destination": "https://github.com/langchain-ai/langchain/tree/master/templates/:path*"
+    },
+    {
+      "source": "/docs/integrations/providers/mlflow_ai_gateway(/?)",
+      "destination": "/docs/integrations/providers/mlflow/"
     }
   ]
 }

libs/cli/langchain_cli/namespaces/migrate/main.py

@@ -5,6 +5,7 @@ from pathlib import Path
 import rich
 import typer
 from gritql import run
+from typer import Option
 
 
 def get_gritdir_path() -> Path:
@@ -15,6 +16,17 @@ def get_gritdir_path() -> Path:
 
 def migrate(
     ctx: typer.Context,
+    # Using diff instead of dry-run for backwards compatibility with the old CLI
+    diff: bool = Option(
+        False,
+        "--diff",
+        help="Show the changes that would be made without applying them.",
+    ),
+    interactive: bool = Option(
+        False,
+        "--interactive",
+        help="Prompt for confirmation before making each change",
+    ),
 ) -> None:
     """Migrate langchain to the most recent version.
 
@@ -47,9 +59,15 @@ def migrate(
     rich.print("-" * 10)
     rich.print()
 
+    args = list(ctx.args)
+    if interactive:
+        args.append("--interactive")
+    if diff:
+        args.append("--dry-run")
+
     final_code = run.apply_pattern(
         "langchain_all_migrations()",
-        ctx.args,
+        args,
         grit_dir=get_gritdir_path(),
     )
 

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-cli"
-version = "0.0.30"
+version = "0.0.31"
 description = "CLI for interacting with LangChain"
 authors = ["Erick Friis <erick@langchain.dev>"]
 readme = "README.md"

libs/community/README.md

@@ -15,7 +15,7 @@ LangChain Community contains third-party integrations that implement the base in
 
 For full documentation see the [API reference](https://api.python.langchain.com/en/stable/community_api_reference.html).
 
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](../../docs/static/svg/langchain_stack_062024.svg "LangChain Framework Overview")
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](https://raw.githubusercontent.com/langchain-ai/langchain/e1d113ea84a2edcf4a7709fc5be0e972ea74a5d9/docs/static/svg/langchain_stack_062024.svg "LangChain Framework Overview")
 
 ## 📕 Releases & Versioning
 

libs/community/langchain_community/agents/openai_assistant/base.py

@@ -301,7 +301,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
             inheritable_metadata=config.get("metadata"),
         )
         run_manager = callback_manager.on_chain_start(
-            dumpd(self), input, name=config.get("run_name")
+            dumpd(self), input, name=config.get("run_name") or self.get_name()
         )
 
         files = _convert_file_ids_into_attachments(kwargs.get("file_ids", []))
@@ -437,7 +437,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
             inheritable_metadata=config.get("metadata"),
         )
         run_manager = callback_manager.on_chain_start(
-            dumpd(self), input, name=config.get("run_name")
+            dumpd(self), input, name=config.get("run_name") or self.get_name()
         )
 
         files = _convert_file_ids_into_attachments(kwargs.get("file_ids", []))

libs/community/langchain_community/callbacks/openai_info.py

@@ -8,6 +8,18 @@ from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration, LLMResult
 
 MODEL_COST_PER_1K_TOKENS = {
+    # OpenAI o1-preview input
+    "o1-preview": 0.015,
+    "o1-preview-2024-09-12": 0.015,
+    # OpenAI o1-preview output
+    "o1-preview-completion": 0.06,
+    "o1-preview-2024-09-12-completion": 0.06,
+    # OpenAI o1-mini input
+    "o1-mini": 0.003,
+    "o1-mini-2024-09-12": 0.003,
+    # OpenAI o1-mini output
+    "o1-mini-completion": 0.012,
+    "o1-mini-2024-09-12-completion": 0.012,
     # GPT-4o-mini input
     "gpt-4o-mini": 0.00015,
     "gpt-4o-mini-2024-07-18": 0.00015,
@@ -153,6 +165,7 @@ def standardize_model_name(
         model_name.startswith("gpt-4")
         or model_name.startswith("gpt-3.5")
         or model_name.startswith("gpt-35")
+        or model_name.startswith("o1-")
         or ("finetuned" in model_name and "legacy" not in model_name)
     ):
         return model_name + "-completion"

libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py

@@ -53,13 +53,15 @@ class LLMThoughtLabeler:
     labeling logic.
     """
 
-    def get_initial_label(self) -> str:
+    @staticmethod
+    def get_initial_label() -> str:
         """Return the markdown label for a new LLMThought that doesn't have
         an associated tool yet.
         """
         return f"{THINKING_EMOJI} **Thinking...**"
 
-    def get_tool_label(self, tool: ToolRecord, is_complete: bool) -> str:
+    @staticmethod
+    def get_tool_label(tool: ToolRecord, is_complete: bool) -> str:
         """Return the label for an LLMThought that has an associated
         tool.
 
@@ -91,13 +93,15 @@ class LLMThoughtLabeler:
         label = f"{emoji} **{name}:** {input}"
         return label
 
-    def get_history_label(self) -> str:
+    @staticmethod
+    def get_history_label() -> str:
         """Return a markdown label for the special 'history' container
         that contains overflow thoughts.
         """
         return f"{HISTORY_EMOJI} **History**"
 
-    def get_final_agent_thought_label(self) -> str:
+    @staticmethod
+    def get_final_agent_thought_label() -> str:
         """Return the markdown label for the agent's final thought -
         the "Now I have the answer" thought, that doesn't involve
         a tool.

libs/community/langchain_community/document_loaders/__init__.py

@@ -359,6 +359,7 @@ if TYPE_CHECKING:
     )
     from langchain_community.document_loaders.pebblo import (
         PebbloSafeLoader,
+        PebbloTextLoader,
     )
     from langchain_community.document_loaders.polars_dataframe import (
         PolarsDataFrameLoader,
@@ -650,6 +651,7 @@ _module_lookup = {
     "PDFPlumberLoader": "langchain_community.document_loaders.pdf",
     "PagedPDFSplitter": "langchain_community.document_loaders.pdf",
     "PebbloSafeLoader": "langchain_community.document_loaders.pebblo",
+    "PebbloTextLoader": "langchain_community.document_loaders.pebblo",
     "PlaywrightURLLoader": "langchain_community.document_loaders.url_playwright",
     "PolarsDataFrameLoader": "langchain_community.document_loaders.polars_dataframe",
     "PsychicLoader": "langchain_community.document_loaders.psychic",
@@ -855,6 +857,7 @@ __all__ = [
     "PDFPlumberLoader",
     "PagedPDFSplitter",
     "PebbloSafeLoader",
+    "PebbloTextLoader",
     "PlaywrightURLLoader",
     "PolarsDataFrameLoader",
     "PsychicLoader",

libs/community/langchain_community/document_loaders/mongodb.py

@@ -20,13 +20,37 @@ class MongodbLoader(BaseLoader):
         *,
         filter_criteria: Optional[Dict] = None,
         field_names: Optional[Sequence[str]] = None,
+        metadata_names: Optional[Sequence[str]] = None,
+        include_db_collection_in_metadata: bool = True,
     ) -> None:
+        """
+        Initializes the MongoDB loader with necessary database connection
+        details and configurations.
+
+        Args:
+            connection_string (str): MongoDB connection URI.
+            db_name (str):Name of the database to connect to.
+            collection_name (str): Name of the collection to fetch documents from.
+            filter_criteria (Optional[Dict]): MongoDB filter criteria for querying
+            documents.
+            field_names (Optional[Sequence[str]]): List of field names to retrieve
+            from documents.
+            metadata_names (Optional[Sequence[str]]): Additional metadata fields to
+            extract from documents.
+            include_db_collection_in_metadata (bool): Flag to include database and
+            collection names in metadata.
+
+        Raises:
+            ImportError: If the motor library is not installed.
+            ValueError: If any necessary argument is missing.
+        """
         try:
             from motor.motor_asyncio import AsyncIOMotorClient
         except ImportError as e:
             raise ImportError(
                 "Cannot import from motor, please install with `pip install motor`."
             ) from e
+
         if not connection_string:
             raise ValueError("connection_string must be provided.")
 
@@ -39,8 +63,10 @@ class MongodbLoader(BaseLoader):
         self.client = AsyncIOMotorClient(connection_string)
         self.db_name = db_name
         self.collection_name = collection_name
-        self.field_names = field_names
+        self.field_names = field_names or []
         self.filter_criteria = filter_criteria or {}
+        self.metadata_names = metadata_names or []
+        self.include_db_collection_in_metadata = include_db_collection_in_metadata
 
         self.db = self.client.get_database(db_name)
         self.collection = self.db.get_collection(collection_name)
@@ -60,36 +86,24 @@ class MongodbLoader(BaseLoader):
         return asyncio.run(self.aload())
 
     async def aload(self) -> List[Document]:
-        """Load data into Document objects."""
+        """Asynchronously loads data into Document objects."""
         result = []
         total_docs = await self.collection.count_documents(self.filter_criteria)
 
-        # Construct the projection dictionary if field_names are specified
-        projection = (
-            {field: 1 for field in self.field_names} if self.field_names else None
-        )
+        projection = self._construct_projection()
 
         async for doc in self.collection.find(self.filter_criteria, projection):
-            metadata = {
-                "database": self.db_name,
-                "collection": self.collection_name,
-            }
+            metadata = self._extract_fields(doc, self.metadata_names, default="")
+
+            # Optionally add database and collection names to metadata
+            if self.include_db_collection_in_metadata:
+                metadata.update(
+                    {"database": self.db_name, "collection": self.collection_name}
+                )
 
             # Extract text content from filtered fields or use the entire document
             if self.field_names is not None:
-                fields = {}
-                for name in self.field_names:
-                    # Split the field names to handle nested fields
-                    keys = name.split(".")
-                    value = doc
-                    for key in keys:
-                        if key in value:
-                            value = value[key]
-                        else:
-                            value = ""
-                            break
-                    fields[name] = value
-
+                fields = self._extract_fields(doc, self.field_names, default="")
                 texts = [str(value) for value in fields.values()]
                 text = " ".join(texts)
             else:
@@ -104,3 +118,29 @@ class MongodbLoader(BaseLoader):
             )
 
         return result
+
+    def _construct_projection(self) -> Optional[Dict]:
+        """Constructs the projection dictionary for MongoDB query based
+        on the specified field names and metadata names."""
+        field_names = list(self.field_names) or []
+        metadata_names = list(self.metadata_names) or []
+        all_fields = field_names + metadata_names
+        return {field: 1 for field in all_fields} if all_fields else None
+
+    def _extract_fields(
+        self,
+        document: Dict,
+        fields: Sequence[str],
+        default: str = "",
+    ) -> Dict:
+        """Extracts and returns values for specified fields from a document."""
+        extracted = {}
+        for field in fields or []:
+            value = document
+            for key in field.split("."):
+                value = value.get(key, default)
+                if value == default:
+                    break
+            new_field_name = field.replace(".", "_")
+            extracted[new_field_name] = value
+        return extracted

libs/community/langchain_community/document_loaders/parsers/pdf.py

@@ -267,6 +267,7 @@ class PyMuPDFParser(BaseBlobParser):
 
     def lazy_parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[valid-type]
         """Lazily parse the blob."""
+
         import fitz
 
         with blob.as_bytes_io() as file_path:  # type: ignore[attr-defined]
@@ -277,25 +278,49 @@ class PyMuPDFParser(BaseBlobParser):
 
             yield from [
                 Document(
-                    page_content=page.get_text(**self.text_kwargs)
-                    + self._extract_images_from_page(doc, page),
-                    metadata=dict(
-                        {
-                            "source": blob.source,  # type: ignore[attr-defined]
-                            "file_path": blob.source,  # type: ignore[attr-defined]
-                            "page": page.number,
-                            "total_pages": len(doc),
-                        },
-                        **{
-                            k: doc.metadata[k]
-                            for k in doc.metadata
-                            if type(doc.metadata[k]) in [str, int]
-                        },
-                    ),
+                    page_content=self._get_page_content(doc, page, blob),
+                    metadata=self._extract_metadata(doc, page, blob),
                 )
                 for page in doc
             ]
 
+    def _get_page_content(
+        self, doc: fitz.fitz.Document, page: fitz.fitz.Page, blob: Blob
+    ) -> str:
+        """
+        Get the text of the page using PyMuPDF and RapidOCR and issue a warning
+        if it is empty.
+        """
+        content = page.get_text(**self.text_kwargs) + self._extract_images_from_page(
+            doc, page
+        )
+
+        if not content:
+            warnings.warn(
+                f"Warning: Empty content on page "
+                f"{page.number} of document {blob.source}"
+            )
+
+        return content
+
+    def _extract_metadata(
+        self, doc: fitz.fitz.Document, page: fitz.fitz.Page, blob: Blob
+    ) -> dict:
+        """Extract metadata from the document and page."""
+        return dict(
+            {
+                "source": blob.source,  # type: ignore[attr-defined]
+                "file_path": blob.source,  # type: ignore[attr-defined]
+                "page": page.number,
+                "total_pages": len(doc),
+            },
+            **{
+                k: doc.metadata[k]
+                for k in doc.metadata
+                if isinstance(doc.metadata[k], (str, int))
+            },
+        )
+
     def _extract_images_from_page(
         self, doc: fitz.fitz.Document, page: fitz.fitz.Page
     ) -> str:

libs/community/langchain_community/document_loaders/pebblo.py

@@ -4,7 +4,7 @@ import logging
 import os
 import uuid
 from importlib.metadata import version
-from typing import Dict, Iterator, List, Optional
+from typing import Any, Dict, Iterable, Iterator, List, Optional
 
 from langchain_core.documents import Document
 
@@ -271,3 +271,67 @@ class PebbloSafeLoader(BaseLoader):
             doc_metadata["pb_checksum"] = classified_docs.get(doc.pb_id, {}).get(
                 "pb_checksum", None
             )
+
+
+class PebbloTextLoader(BaseLoader):
+    """
+    Loader for text data.
+
+    Since PebbloSafeLoader is a wrapper around document loaders, this loader is
+    used to load text data directly into Documents.
+    """
+
+    def __init__(
+        self,
+        texts: Iterable[str],
+        *,
+        source: Optional[str] = None,
+        ids: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+    ) -> None:
+        """
+        Args:
+            texts: Iterable of text data.
+            source: Source of the text data.
+                Optional. Defaults to None.
+            ids: List of unique identifiers for each text.
+                Optional. Defaults to None.
+            metadata: Metadata for all texts.
+                Optional. Defaults to None.
+            metadatas: List of metadata for each text.
+                Optional. Defaults to None.
+        """
+        self.texts = texts
+        self.source = source
+        self.ids = ids
+        self.metadata = metadata
+        self.metadatas = metadatas
+
+    def lazy_load(self) -> Iterator[Document]:
+        """
+        Lazy load text data into Documents.
+
+        Returns:
+            Iterator of Documents
+        """
+        for i, text in enumerate(self.texts):
+            _id = None
+            metadata = self.metadata or {}
+            if self.metadatas and i < len(self.metadatas) and self.metadatas[i]:
+                metadata.update(self.metadatas[i])
+            if self.ids and i < len(self.ids):
+                _id = self.ids[i]
+            yield Document(id=_id, page_content=text, metadata=metadata)
+
+    def load(self) -> List[Document]:
+        """
+        Load text data into Documents.
+
+        Returns:
+            List of Documents
+        """
+        documents = []
+        for doc in self.lazy_load():
+            documents.append(doc)
+        return documents

libs/community/langchain_community/document_loaders/recursive_url_loader.py

@@ -227,7 +227,7 @@ class RecursiveUrlLoader(BaseLoader):
                 "https://docs.python.org/3.9/",
                 prevent_outside=True,
                 base_url="https://docs.python.org",
-                link_regex=r'<a\s+(?:[^>]*?\s+)?href="([^"]*(?=index)[^"]*)"',
+                link_regex=r'<a\\s+(?:[^>]*?\\s+)?href="([^"]*(?=index)[^"]*)"',
                 exclude_dirs=['https://docs.python.org/3.9/faq']
             )
             docs = loader.load()

libs/community/langchain_community/document_transformers/beautiful_soup_transformer.py

@@ -132,6 +132,7 @@ class BeautifulSoupTransformer(BaseDocumentTransformer):
         Args:
             html_content: The original HTML content string.
             tags: A list of tags to be extracted from the HTML.
+            remove_comments: If set to True, the comments will be removed.
 
         Returns:
             A string combining the content of the extracted tags.
@@ -184,6 +185,7 @@ def get_navigable_strings(
 
     Args:
         element: A BeautifulSoup element.
+        remove_comments: If set to True, the comments will be removed.
 
     Returns:
         A generator of strings.

libs/community/langchain_community/embeddings/sambanova.py

@@ -213,7 +213,7 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                     )
                 try:
                     if params.get("select_expert"):
-                        embedding = response.json()["predictions"][0]
+                        embedding = response.json()["predictions"]
                     else:
                         embedding = response.json()["predictions"]
                     embeddings.extend(embedding)
@@ -299,7 +299,7 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
                 )
             try:
                 if params.get("select_expert"):
-                    embedding = response.json()["predictions"][0][0]
+                    embedding = response.json()["predictions"][0]
                 else:
                     embedding = response.json()["predictions"][0]
             except KeyError:

libs/community/langchain_community/graph_vectorstores/base.py

@@ -1,7 +1,708 @@
-from langchain_core.graph_vectorstores.base import (
-    GraphVectorStore,
-    GraphVectorStoreRetriever,
-    Node,
+from __future__ import annotations
+
+from abc import abstractmethod
+from collections.abc import AsyncIterable, Collection, Iterable, Iterator
+from typing import (
+    Any,
+    ClassVar,
+    Optional,
 )
 
-__all__ = ["GraphVectorStore", "GraphVectorStoreRetriever", "Node"]
+from langchain_core._api import beta
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
+from langchain_core.documents import Document
+from langchain_core.load import Serializable
+from langchain_core.runnables import run_in_executor
+from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
+from pydantic import Field
+
+from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
+
+
+def _has_next(iterator: Iterator) -> bool:
+    """Checks if the iterator has more elements.
+    Warning: consumes an element from the iterator"""
+    sentinel = object()
+    return next(iterator, sentinel) is not sentinel
+
+
+@beta()
+class Node(Serializable):
+    """Node in the GraphVectorStore.
+
+    Edges exist from nodes with an outgoing link to nodes with a matching incoming link.
+
+    For instance two nodes `a` and `b` connected over a hyperlink ``https://some-url``
+    would look like:
+
+    .. code-block:: python
+
+        [
+            Node(
+                id="a",
+                text="some text a",
+                links= [
+                    Link(kind="hyperlink", tag="https://some-url", direction="incoming")
+                ],
+            ),
+            Node(
+                id="b",
+                text="some text b",
+                links= [
+                    Link(kind="hyperlink", tag="https://some-url", direction="outgoing")
+                ],
+            )
+        ]
+    """
+
+    id: Optional[str] = None
+    """Unique ID for the node. Will be generated by the GraphVectorStore if not set."""
+    text: str
+    """Text contained by the node."""
+    metadata: dict = Field(default_factory=dict)
+    """Metadata for the node."""
+    links: list[Link] = Field(default_factory=list)
+    """Links associated with the node."""
+
+
+def _texts_to_nodes(
+    texts: Iterable[str],
+    metadatas: Optional[Iterable[dict]],
+    ids: Optional[Iterable[str]],
+) -> Iterator[Node]:
+    metadatas_it = iter(metadatas) if metadatas else None
+    ids_it = iter(ids) if ids else None
+    for text in texts:
+        try:
+            _metadata = next(metadatas_it).copy() if metadatas_it else {}
+        except StopIteration as e:
+            raise ValueError("texts iterable longer than metadatas") from e
+        try:
+            _id = next(ids_it) if ids_it else None
+        except StopIteration as e:
+            raise ValueError("texts iterable longer than ids") from e
+
+        links = _metadata.pop(METADATA_LINKS_KEY, [])
+        if not isinstance(links, list):
+            links = list(links)
+        yield Node(
+            id=_id,
+            metadata=_metadata,
+            text=text,
+            links=links,
+        )
+    if ids_it and _has_next(ids_it):
+        raise ValueError("ids iterable longer than texts")
+    if metadatas_it and _has_next(metadatas_it):
+        raise ValueError("metadatas iterable longer than texts")
+
+
+def _documents_to_nodes(documents: Iterable[Document]) -> Iterator[Node]:
+    for doc in documents:
+        metadata = doc.metadata.copy()
+        links = metadata.pop(METADATA_LINKS_KEY, [])
+        if not isinstance(links, list):
+            links = list(links)
+        yield Node(
+            id=doc.id,
+            metadata=metadata,
+            text=doc.page_content,
+            links=links,
+        )
+
+
+@beta()
+def nodes_to_documents(nodes: Iterable[Node]) -> Iterator[Document]:
+    """Convert nodes to documents.
+
+    Args:
+        nodes: The nodes to convert to documents.
+    Returns:
+        The documents generated from the nodes.
+    """
+    for node in nodes:
+        metadata = node.metadata.copy()
+        metadata[METADATA_LINKS_KEY] = [
+            # Convert the core `Link` (from the node) back to the local `Link`.
+            Link(kind=link.kind, direction=link.direction, tag=link.tag)
+            for link in node.links
+        ]
+
+        yield Document(
+            id=node.id,
+            page_content=node.text,
+            metadata=metadata,
+        )
+
+
+@beta(message="Added in version 0.2.14 of langchain_core. API subject to change.")
+class GraphVectorStore(VectorStore):
+    """A hybrid vector-and-graph graph store.
+
+    Document chunks support vector-similarity search as well as edges linking
+    chunks based on structural and semantic properties.
+
+    .. versionadded:: 0.2.14
+    """
+
+    @abstractmethod
+    def add_nodes(
+        self,
+        nodes: Iterable[Node],
+        **kwargs: Any,
+    ) -> Iterable[str]:
+        """Add nodes to the graph store.
+
+        Args:
+            nodes: the nodes to add.
+        """
+
+    async def aadd_nodes(
+        self,
+        nodes: Iterable[Node],
+        **kwargs: Any,
+    ) -> AsyncIterable[str]:
+        """Add nodes to the graph store.
+
+        Args:
+            nodes: the nodes to add.
+        """
+        iterator = iter(await run_in_executor(None, self.add_nodes, nodes, **kwargs))
+        done = object()
+        while True:
+            doc = await run_in_executor(None, next, iterator, done)
+            if doc is done:
+                break
+            yield doc  # type: ignore[misc]
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[Iterable[dict]] = None,
+        *,
+        ids: Optional[Iterable[str]] = None,
+        **kwargs: Any,
+    ) -> list[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        The Links present in the metadata field `links` will be extracted to create
+        the `Node` links.
+
+        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
+        function call would look like:
+
+        .. code-block:: python
+
+            store.add_texts(
+                ids=["a", "b"],
+                texts=["some text a", "some text b"],
+                metadatas=[
+                    {
+                        "links": [
+                            Link.incoming(kind="hyperlink", tag="https://some-url")
+                        ]
+                    },
+                    {
+                        "links": [
+                            Link.outgoing(kind="hyperlink", tag="https://some-url")
+                        ]
+                    },
+                ],
+            )
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+                The metadata key `links` shall be an iterable of
+                :py:class:`~langchain_community.graph_vectorstores.links.Link`.
+            **kwargs: vectorstore specific parameters.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        nodes = _texts_to_nodes(texts, metadatas, ids)
+        return list(self.add_nodes(nodes, **kwargs))
+
+    async def aadd_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[Iterable[dict]] = None,
+        *,
+        ids: Optional[Iterable[str]] = None,
+        **kwargs: Any,
+    ) -> list[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        The Links present in the metadata field `links` will be extracted to create
+        the `Node` links.
+
+        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
+        function call would look like:
+
+        .. code-block:: python
+
+            await store.aadd_texts(
+                ids=["a", "b"],
+                texts=["some text a", "some text b"],
+                metadatas=[
+                    {
+                        "links": [
+                            Link.incoming(kind="hyperlink", tag="https://some-url")
+                        ]
+                    },
+                    {
+                        "links": [
+                            Link.outgoing(kind="hyperlink", tag="https://some-url")
+                        ]
+                    },
+                ],
+            )
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+                The metadata key `links` shall be an iterable of
+                :py:class:`~langchain_community.graph_vectorstores.links.Link`.
+            **kwargs: vectorstore specific parameters.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        nodes = _texts_to_nodes(texts, metadatas, ids)
+        return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
+
+    def add_documents(
+        self,
+        documents: Iterable[Document],
+        **kwargs: Any,
+    ) -> list[str]:
+        """Run more documents through the embeddings and add to the vectorstore.
+
+        The Links present in the document metadata field `links` will be extracted to
+        create the `Node` links.
+
+        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
+        function call would look like:
+
+        .. code-block:: python
+
+            store.add_documents(
+                [
+                    Document(
+                        id="a",
+                        page_content="some text a",
+                        metadata={
+                            "links": [
+                                Link.incoming(kind="hyperlink", tag="http://some-url")
+                            ]
+                        }
+                    ),
+                    Document(
+                        id="b",
+                        page_content="some text b",
+                        metadata={
+                            "links": [
+                                Link.outgoing(kind="hyperlink", tag="http://some-url")
+                            ]
+                        }
+                    ),
+                ]
+
+            )
+
+        Args:
+            documents: Documents to add to the vectorstore.
+                The document's metadata key `links` shall be an iterable of
+                :py:class:`~langchain_community.graph_vectorstores.links.Link`.
+
+        Returns:
+            List of IDs of the added texts.
+        """
+        nodes = _documents_to_nodes(documents)
+        return list(self.add_nodes(nodes, **kwargs))
+
+    async def aadd_documents(
+        self,
+        documents: Iterable[Document],
+        **kwargs: Any,
+    ) -> list[str]:
+        """Run more documents through the embeddings and add to the vectorstore.
+
+        The Links present in the document metadata field `links` will be extracted to
+        create the `Node` links.
+
+        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
+        function call would look like:
+
+        .. code-block:: python
+
+            store.add_documents(
+                [
+                    Document(
+                        id="a",
+                        page_content="some text a",
+                        metadata={
+                            "links": [
+                                Link.incoming(kind="hyperlink", tag="http://some-url")
+                            ]
+                        }
+                    ),
+                    Document(
+                        id="b",
+                        page_content="some text b",
+                        metadata={
+                            "links": [
+                                Link.outgoing(kind="hyperlink", tag="http://some-url")
+                            ]
+                        }
+                    ),
+                ]
+
+            )
+
+        Args:
+            documents: Documents to add to the vectorstore.
+                The document's metadata key `links` shall be an iterable of
+                :py:class:`~langchain_community.graph_vectorstores.links.Link`.
+
+        Returns:
+            List of IDs of the added texts.
+        """
+        nodes = _documents_to_nodes(documents)
+        return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
+
+    @abstractmethod
+    def traversal_search(
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 1,
+        **kwargs: Any,
+    ) -> Iterable[Document]:
+        """Retrieve documents from traversing this graph store.
+
+        First, `k` nodes are retrieved using a search for each `query` string.
+        Then, additional nodes are discovered up to the given `depth` from those
+        starting nodes.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return from the initial search.
+                Defaults to 4. Applies to each of the query strings.
+            depth: The maximum depth of edges to traverse. Defaults to 1.
+        Returns:
+            Retrieved documents.
+        """
+
+    async def atraversal_search(
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 1,
+        **kwargs: Any,
+    ) -> AsyncIterable[Document]:
+        """Retrieve documents from traversing this graph store.
+
+        First, `k` nodes are retrieved using a search for each `query` string.
+        Then, additional nodes are discovered up to the given `depth` from those
+        starting nodes.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return from the initial search.
+                Defaults to 4. Applies to each of the query strings.
+            depth: The maximum depth of edges to traverse. Defaults to 1.
+        Returns:
+            Retrieved documents.
+        """
+        iterator = iter(
+            await run_in_executor(
+                None, self.traversal_search, query, k=k, depth=depth, **kwargs
+            )
+        )
+        done = object()
+        while True:
+            doc = await run_in_executor(None, next, iterator, done)
+            if doc is done:
+                break
+            yield doc  # type: ignore[misc]
+
+    @abstractmethod
+    def mmr_traversal_search(
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 2,
+        fetch_k: int = 100,
+        adjacent_k: int = 10,
+        lambda_mult: float = 0.5,
+        score_threshold: float = float("-inf"),
+        **kwargs: Any,
+    ) -> Iterable[Document]:
+        """Retrieve documents from this graph store using MMR-traversal.
+
+        This strategy first retrieves the top `fetch_k` results by similarity to
+        the question. It then selects the top `k` results based on
+        maximum-marginal relevance using the given `lambda_mult`.
+
+        At each step, it considers the (remaining) documents from `fetch_k` as
+        well as any documents connected by edges to a selected document
+        retrieved based on similarity (a "root").
+
+        Args:
+            query: The query string to search for.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch via similarity.
+                Defaults to 100.
+            adjacent_k: Number of adjacent Documents to fetch.
+                Defaults to 10.
+            depth: Maximum depth of a node (number of edges) from a node
+                retrieved via similarity. Defaults to 2.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding to maximum
+                diversity and 1 to minimum diversity. Defaults to 0.5.
+            score_threshold: Only documents with a score greater than or equal
+                this threshold will be chosen. Defaults to negative infinity.
+        """
+
+    async def ammr_traversal_search(
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 2,
+        fetch_k: int = 100,
+        adjacent_k: int = 10,
+        lambda_mult: float = 0.5,
+        score_threshold: float = float("-inf"),
+        **kwargs: Any,
+    ) -> AsyncIterable[Document]:
+        """Retrieve documents from this graph store using MMR-traversal.
+
+        This strategy first retrieves the top `fetch_k` results by similarity to
+        the question. It then selects the top `k` results based on
+        maximum-marginal relevance using the given `lambda_mult`.
+
+        At each step, it considers the (remaining) documents from `fetch_k` as
+        well as any documents connected by edges to a selected document
+        retrieved based on similarity (a "root").
+
+        Args:
+            query: The query string to search for.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch via similarity.
+                Defaults to 100.
+            adjacent_k: Number of adjacent Documents to fetch.
+                Defaults to 10.
+            depth: Maximum depth of a node (number of edges) from a node
+                retrieved via similarity. Defaults to 2.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding to maximum
+                diversity and 1 to minimum diversity. Defaults to 0.5.
+            score_threshold: Only documents with a score greater than or equal
+                this threshold will be chosen. Defaults to negative infinity.
+        """
+        iterator = iter(
+            await run_in_executor(
+                None,
+                self.mmr_traversal_search,
+                query,
+                k=k,
+                fetch_k=fetch_k,
+                adjacent_k=adjacent_k,
+                depth=depth,
+                lambda_mult=lambda_mult,
+                score_threshold=score_threshold,
+                **kwargs,
+            )
+        )
+        done = object()
+        while True:
+            doc = await run_in_executor(None, next, iterator, done)
+            if doc is done:
+                break
+            yield doc  # type: ignore[misc]
+
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> list[Document]:
+        return list(self.traversal_search(query, k=k, depth=0))
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> list[Document]:
+        return list(
+            self.mmr_traversal_search(
+                query, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, depth=0
+            )
+        )
+
+    async def asimilarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> list[Document]:
+        return [doc async for doc in self.atraversal_search(query, k=k, depth=0)]
+
+    def search(self, query: str, search_type: str, **kwargs: Any) -> list[Document]:
+        if search_type == "similarity":
+            return self.similarity_search(query, **kwargs)
+        elif search_type == "similarity_score_threshold":
+            docs_and_similarities = self.similarity_search_with_relevance_scores(
+                query, **kwargs
+            )
+            return [doc for doc, _ in docs_and_similarities]
+        elif search_type == "mmr":
+            return self.max_marginal_relevance_search(query, **kwargs)
+        elif search_type == "traversal":
+            return list(self.traversal_search(query, **kwargs))
+        elif search_type == "mmr_traversal":
+            return list(self.mmr_traversal_search(query, **kwargs))
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity', 'similarity_score_threshold', "
+                "'mmr' or 'traversal'."
+            )
+
+    async def asearch(
+        self, query: str, search_type: str, **kwargs: Any
+    ) -> list[Document]:
+        if search_type == "similarity":
+            return await self.asimilarity_search(query, **kwargs)
+        elif search_type == "similarity_score_threshold":
+            docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
+                query, **kwargs
+            )
+            return [doc for doc, _ in docs_and_similarities]
+        elif search_type == "mmr":
+            return await self.amax_marginal_relevance_search(query, **kwargs)
+        elif search_type == "traversal":
+            return [doc async for doc in self.atraversal_search(query, **kwargs)]
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity', 'similarity_score_threshold', "
+                "'mmr' or 'traversal'."
+            )
+
+    def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
+        """Return GraphVectorStoreRetriever initialized from this GraphVectorStore.
+
+        Args:
+            **kwargs: Keyword arguments to pass to the search function.
+                Can include:
+
+                - search_type (Optional[str]): Defines the type of search that
+                  the Retriever should perform.
+                  Can be ``traversal`` (default), ``similarity``, ``mmr``, or
+                  ``similarity_score_threshold``.
+                - search_kwargs (Optional[Dict]): Keyword arguments to pass to the
+                  search function. Can include things like:
+
+                  - k(int): Amount of documents to return (Default: 4).
+                  - depth(int): The maximum depth of edges to traverse (Default: 1).
+                  - score_threshold(float): Minimum relevance threshold
+                    for similarity_score_threshold.
+                  - fetch_k(int): Amount of documents to pass to MMR algorithm
+                    (Default: 20).
+                  - lambda_mult(float): Diversity of results returned by MMR;
+                    1 for minimum diversity and 0 for maximum. (Default: 0.5).
+        Returns:
+            Retriever for this GraphVectorStore.
+
+        Examples:
+
+        .. code-block:: python
+
+            # Retrieve documents traversing edges
+            docsearch.as_retriever(
+                search_type="traversal",
+                search_kwargs={'k': 6, 'depth': 3}
+            )
+
+            # Retrieve more documents with higher diversity
+            # Useful if your dataset has many similar documents
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 6, 'lambda_mult': 0.25}
+            )
+
+            # Fetch more documents for the MMR algorithm to consider
+            # But only return the top 5
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 5, 'fetch_k': 50}
+            )
+
+            # Only retrieve documents that have a relevance score
+            # Above a certain threshold
+            docsearch.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={'score_threshold': 0.8}
+            )
+
+            # Only get the single most similar document from the dataset
+            docsearch.as_retriever(search_kwargs={'k': 1})
+
+        """
+        return GraphVectorStoreRetriever(vectorstore=self, **kwargs)
+
+
+class GraphVectorStoreRetriever(VectorStoreRetriever):
+    """Retriever class for GraphVectorStore."""
+
+    vectorstore: GraphVectorStore
+    """GraphVectorStore to use for retrieval."""
+    search_type: str = "traversal"
+    """Type of search to perform. Defaults to "traversal"."""
+    allowed_search_types: ClassVar[Collection[str]] = (
+        "similarity",
+        "similarity_score_threshold",
+        "mmr",
+        "traversal",
+        "mmr_traversal",
+    )
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> list[Document]:
+        if self.search_type == "traversal":
+            return list(self.vectorstore.traversal_search(query, **self.search_kwargs))
+        elif self.search_type == "mmr_traversal":
+            return list(
+                self.vectorstore.mmr_traversal_search(query, **self.search_kwargs)
+            )
+        else:
+            return super()._get_relevant_documents(query, run_manager=run_manager)
+
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> list[Document]:
+        if self.search_type == "traversal":
+            return [
+                doc
+                async for doc in self.vectorstore.atraversal_search(
+                    query, **self.search_kwargs
+                )
+            ]
+        elif self.search_type == "mmr_traversal":
+            return [
+                doc
+                async for doc in self.vectorstore.ammr_traversal_search(
+                    query, **self.search_kwargs
+                )
+            ]
+        else:
+            return await super()._aget_relevant_documents(
+                query, run_manager=run_manager
+            )

libs/community/langchain_community/graph_vectorstores/cassandra.py

@@ -12,12 +12,12 @@ from typing import (
 from langchain_core._api import beta
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.graph_vectorstores.base import (
+
+from langchain_community.graph_vectorstores.base import (
     GraphVectorStore,
     Node,
     nodes_to_documents,
 )
-
 from langchain_community.utilities.cassandra import SetupMode
 
 if TYPE_CHECKING:

libs/community/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py

@@ -2,11 +2,11 @@ from typing import Any, Dict, Iterable, List, Optional, Set, Union
 
 from langchain_core._api import beta
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.links import Link
 
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
 )
+from langchain_community.graph_vectorstores.links import Link
 
 # TypeAlias is not available in Python 3.9, we can't use that or the newer `type`.
 GLiNERInput = Union[str, Document]
@@ -34,7 +34,7 @@ class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
     .. seealso::
 
             - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
-            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+            - :class:`How to create links between documents <langchain_community.graph_vectorstores.links.Link>`
 
     How to link Documents on common named entities
     ==============================================
@@ -59,12 +59,12 @@ class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
 
     We can use :meth:`extract_one` on a document to get the links and add the links
     to the document metadata with
-    :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+    :meth:`~langchain_community.graph_vectorstores.links.add_links`::
 
         from langchain_community.document_loaders import TextLoader
         from langchain_community.graph_vectorstores import CassandraGraphVectorStore
         from langchain_community.graph_vectorstores.extractors import GLiNERLinkExtractor
-        from langchain_core.graph_vectorstores.links import add_links
+        from langchain_community.graph_vectorstores.links import add_links
         from langchain_text_splitters import CharacterTextSplitter
 
         loader = TextLoader("state_of_the_union.txt")
@@ -87,7 +87,7 @@ class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
     Using LinkExtractorTransformer
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+    Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`,
     we can simplify the link extraction::
 
         from langchain_community.document_loaders import TextLoader
@@ -113,7 +113,7 @@ class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
 
         {'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]}
 
-    The documents with named entity links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+    The documents with named entity links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`::
 
         from langchain_community.graph_vectorstores import CassandraGraphVectorStore
 

libs/community/langchain_community/graph_vectorstores/extractors/hierarchy_link_extractor.py

@@ -2,7 +2,6 @@ from typing import Callable, List, Set
 
 from langchain_core._api import beta
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.links import Link
 
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
@@ -10,6 +9,7 @@ from langchain_community.graph_vectorstores.extractors.link_extractor import (
 from langchain_community.graph_vectorstores.extractors.link_extractor_adapter import (
     LinkExtractorAdapter,
 )
+from langchain_community.graph_vectorstores.links import Link
 
 # TypeAlias is not available in Python 3.9, we can't use that or the newer `type`.
 HierarchyInput = List[str]

libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py

@@ -6,8 +6,8 @@ from urllib.parse import urldefrag, urljoin, urlparse
 
 from langchain_core._api import beta
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores import Link
 
+from langchain_community.graph_vectorstores import Link
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
 )
@@ -77,7 +77,7 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
         .. seealso::
 
             - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
-            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+            - :class:`How to create links between documents <langchain_community.graph_vectorstores.links.Link>`
 
         How to link Documents on hyperlinks in HTML
         ===========================================
@@ -103,7 +103,7 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
 
         We can use :meth:`extract_one` on a document to get the links and add the links
         to the document metadata with
-        :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+        :meth:`~langchain_community.graph_vectorstores.links.add_links`::
 
             from langchain_community.document_loaders import AsyncHtmlLoader
             from langchain_community.graph_vectorstores.extractors import (
@@ -148,7 +148,7 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
 
             from langchain_community.document_loaders import AsyncHtmlLoader
             from langchain_community.graph_vectorstores.extractors import HtmlLinkExtractor
-            from langchain_core.graph_vectorstores.links import add_links
+            from langchain_community.graph_vectorstores.links import add_links
 
             loader = AsyncHtmlLoader(
                 [
@@ -176,7 +176,7 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
         Using LinkExtractorTransformer
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-        Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+        Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`,
         we can simplify the link extraction::
 
             from langchain_community.document_loaders import AsyncHtmlLoader
@@ -227,7 +227,7 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
 
             Found link from https://python.langchain.com/v0.2/docs/integrations/providers/astradb/ to https://docs.datastax.com/en/astra/home/astra.html.
 
-        The documents with URL links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+        The documents with URL links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`::
 
             from langchain_community.graph_vectorstores import CassandraGraphVectorStore
 

libs/community/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py

@@ -2,11 +2,11 @@ from typing import Any, Dict, Iterable, Optional, Set, Union
 
 from langchain_core._api import beta
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.links import Link
 
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
 )
+from langchain_community.graph_vectorstores.links import Link
 
 KeybertInput = Union[str, Document]
 
@@ -37,7 +37,7 @@ class KeybertLinkExtractor(LinkExtractor[KeybertInput]):
         .. seealso::
 
             - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
-            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+            - :class:`How to create links between documents <langchain_community.graph_vectorstores.links.Link>`
 
         How to link Documents on common keywords using Keybert
         ======================================================
@@ -62,12 +62,12 @@ class KeybertLinkExtractor(LinkExtractor[KeybertInput]):
 
         We can use :meth:`extract_one` on a document to get the links and add the links
         to the document metadata with
-        :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+        :meth:`~langchain_community.graph_vectorstores.links.add_links`::
 
             from langchain_community.document_loaders import TextLoader
             from langchain_community.graph_vectorstores import CassandraGraphVectorStore
             from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
-            from langchain_core.graph_vectorstores.links import add_links
+            from langchain_community.graph_vectorstores.links import add_links
             from langchain_text_splitters import CharacterTextSplitter
 
             loader = TextLoader("state_of_the_union.txt")
@@ -91,7 +91,7 @@ class KeybertLinkExtractor(LinkExtractor[KeybertInput]):
         Using LinkExtractorTransformer
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-        Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+        Using the :class:`~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer`,
         we can simplify the link extraction::
 
             from langchain_community.document_loaders import TextLoader
@@ -116,7 +116,7 @@ class KeybertLinkExtractor(LinkExtractor[KeybertInput]):
 
             {'source': 'state_of_the_union.txt', 'links': [Link(kind='kw', direction='bidir', tag='ukraine'), Link(kind='kw', direction='bidir', tag='ukrainian'), Link(kind='kw', direction='bidir', tag='putin'), Link(kind='kw', direction='bidir', tag='vladimir'), Link(kind='kw', direction='bidir', tag='russia')]}
 
-        The documents with keyword links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+        The documents with keyword links can then be added to a :class:`~langchain_community.graph_vectorstores.base.GraphVectorStore`::
 
             from langchain_community.graph_vectorstores import CassandraGraphVectorStore
 

libs/community/langchain_community/graph_vectorstores/extractors/link_extractor.py

@@ -4,7 +4,8 @@ from abc import ABC, abstractmethod
 from typing import Generic, Iterable, Set, TypeVar
 
 from langchain_core._api import beta
-from langchain_core.graph_vectorstores import Link
+
+from langchain_community.graph_vectorstores import Link
 
 InputT = TypeVar("InputT")
 

libs/community/langchain_community/graph_vectorstores/extractors/link_extractor_adapter.py

@@ -1,8 +1,8 @@
 from typing import Callable, Iterable, Set, TypeVar
 
 from langchain_core._api import beta
-from langchain_core.graph_vectorstores import Link
 
+from langchain_community.graph_vectorstores import Link
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
 )

libs/community/langchain_community/graph_vectorstores/extractors/link_extractor_transformer.py

@@ -3,11 +3,11 @@ from typing import Any, Sequence
 from langchain_core._api import beta
 from langchain_core.documents import Document
 from langchain_core.documents.transformers import BaseDocumentTransformer
-from langchain_core.graph_vectorstores.links import copy_with_links
 
 from langchain_community.graph_vectorstores.extractors.link_extractor import (
     LinkExtractor,
 )
+from langchain_community.graph_vectorstores.links import copy_with_links
 
 
 @beta()

libs/community/langchain_community/graph_vectorstores/links.py

@@ -1,8 +1,102 @@
-from langchain_core.graph_vectorstores.links import (
-    Link,
-    add_links,
-    copy_with_links,
-    get_links,
-)
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import Literal, Union
 
-__all__ = ["Link", "add_links", "get_links", "copy_with_links"]
+from langchain_core._api import beta
+from langchain_core.documents import Document
+
+
+@beta()
+@dataclass(frozen=True)
+class Link:
+    """A link to/from a tag of a given tag.
+
+    Edges exist from nodes with an outgoing link to nodes with a matching incoming link.
+    """
+
+    kind: str
+    """The kind of link. Allows different extractors to use the same tag name without
+    creating collisions between extractors. For example “keyword” vs “url”."""
+    direction: Literal["in", "out", "bidir"]
+    """The direction of the link."""
+    tag: str
+    """The tag of the link."""
+
+    @staticmethod
+    def incoming(kind: str, tag: str) -> "Link":
+        """Create an incoming link."""
+        return Link(kind=kind, direction="in", tag=tag)
+
+    @staticmethod
+    def outgoing(kind: str, tag: str) -> "Link":
+        """Create an outgoing link."""
+        return Link(kind=kind, direction="out", tag=tag)
+
+    @staticmethod
+    def bidir(kind: str, tag: str) -> "Link":
+        """Create a bidirectional link."""
+        return Link(kind=kind, direction="bidir", tag=tag)
+
+
+METADATA_LINKS_KEY = "links"
+
+
+@beta()
+def get_links(doc: Document) -> list[Link]:
+    """Get the links from a document.
+
+    Args:
+        doc: The document to get the link tags from.
+    Returns:
+        The set of link tags from the document.
+    """
+
+    links = doc.metadata.setdefault(METADATA_LINKS_KEY, [])
+    if not isinstance(links, list):
+        # Convert to a list and remember that.
+        links = list(links)
+        doc.metadata[METADATA_LINKS_KEY] = links
+    return links
+
+
+@beta()
+def add_links(doc: Document, *links: Union[Link, Iterable[Link]]) -> None:
+    """Add links to the given metadata.
+
+    Args:
+        doc: The document to add the links to.
+        *links: The links to add to the document.
+    """
+    links_in_metadata = get_links(doc)
+    for link in links:
+        if isinstance(link, Iterable):
+            links_in_metadata.extend(link)
+        else:
+            links_in_metadata.append(link)
+
+
+@beta()
+def copy_with_links(doc: Document, *links: Union[Link, Iterable[Link]]) -> Document:
+    """Return a document with the given links added.
+
+    Args:
+        doc: The document to add the links to.
+        *links: The links to add to the document.
+
+    Returns:
+        A document with a shallow-copy of the metadata with the links added.
+    """
+    new_links = set(get_links(doc))
+    for link in links:
+        if isinstance(link, Iterable):
+            new_links.update(link)
+        else:
+            new_links.add(link)
+
+    return Document(
+        page_content=doc.page_content,
+        metadata={
+            **doc.metadata,
+            METADATA_LINKS_KEY: list(new_links),
+        },
+    )

libs/community/langchain_community/graphs/neo4j_graph.py

@@ -411,7 +411,9 @@ class Neo4jGraph(GraphStore):
         return self.structured_schema
 
     def query(
-        self, query: str, params: dict = {}, retry_on_session_expired: bool = True
+        self,
+        query: str,
+        params: dict = {},
     ) -> List[Dict[str, Any]]:
         """Query Neo4j database.
 
@@ -423,26 +425,44 @@ class Neo4jGraph(GraphStore):
             List[Dict[str, Any]]: The list of dictionaries containing the query results.
         """
         from neo4j import Query
-        from neo4j.exceptions import CypherSyntaxError, SessionExpired
+        from neo4j.exceptions import Neo4jError
 
-        with self._driver.session(database=self._database) as session:
-            try:
-                data = session.run(Query(text=query, timeout=self.timeout), params)
-                json_data = [r.data() for r in data]
-                if self.sanitize:
-                    json_data = [value_sanitize(el) for el in json_data]
-                return json_data
-            except CypherSyntaxError as e:
-                raise ValueError(f"Generated Cypher Statement is not valid\n{e}")
-            except (
-                SessionExpired
-            ) as e:  # Session expired is a transient error that can be retried
-                if retry_on_session_expired:
-                    return self.query(
-                        query, params=params, retry_on_session_expired=False
+        try:
+            data, _, _ = self._driver.execute_query(
+                Query(text=query, timeout=self.timeout),
+                database=self._database,
+                parameters_=params,
+            )
+            json_data = [r.data() for r in data]
+            if self.sanitize:
+                json_data = [value_sanitize(el) for el in json_data]
+            return json_data
+        except Neo4jError as e:
+            if not (
+                (
+                    (  # isCallInTransactionError
+                        e.code == "Neo.DatabaseError.Statement.ExecutionFailed"
+                        or e.code
+                        == "Neo.DatabaseError.Transaction.TransactionStartFailed"
                     )
-                else:
-                    raise e
+                    and "in an implicit transaction" in e.message
+                )
+                or (  # isPeriodicCommitError
+                    e.code == "Neo.ClientError.Statement.SemanticError"
+                    and (
+                        "in an open transaction is not possible" in e.message
+                        or "tried to execute in an explicit transaction" in e.message
+                    )
+                )
+            ):
+                raise
+        # fallback to allow implicit transactions
+        with self._driver.session() as session:
+            data = session.run(Query(text=query, timeout=self.timeout), params)
+            json_data = [r.data() for r in data]
+            if self.sanitize:
+                json_data = [value_sanitize(el) for el in json_data]
+            return json_data
 
     def refresh_schema(self) -> None:
         """

libs/community/langchain_community/llms/__init__.py

@@ -510,12 +510,6 @@ def _import_sagemaker_endpoint() -> Type[BaseLLM]:
     return SagemakerEndpoint
 
 
-def _import_sambaverse() -> Type[BaseLLM]:
-    from langchain_community.llms.sambanova import Sambaverse
-
-    return Sambaverse
-
-
 def _import_sambastudio() -> Type[BaseLLM]:
     from langchain_community.llms.sambanova import SambaStudio
 
@@ -817,8 +811,6 @@ def __getattr__(name: str) -> Any:
         return _import_rwkv()
     elif name == "SagemakerEndpoint":
         return _import_sagemaker_endpoint()
-    elif name == "Sambaverse":
-        return _import_sambaverse()
     elif name == "SambaStudio":
         return _import_sambastudio()
     elif name == "SelfHostedPipeline":
@@ -954,7 +946,6 @@ __all__ = [
     "RWKV",
     "Replicate",
     "SagemakerEndpoint",
-    "Sambaverse",
     "SambaStudio",
     "SelfHostedHuggingFaceLLM",
     "SelfHostedPipeline",
@@ -1051,7 +1042,6 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
         "replicate": _import_replicate,
         "rwkv": _import_rwkv,
         "sagemaker_endpoint": _import_sagemaker_endpoint,
-        "sambaverse": _import_sambaverse,
         "sambastudio": _import_sambastudio,
         "self_hosted": _import_self_hosted,
         "self_hosted_hugging_face": _import_self_hosted_hugging_face,

libs/community/langchain_community/llms/sambanova.py

@@ -9,464 +9,6 @@ from langchain_core.utils import get_from_dict_or_env, pre_init
 from pydantic import ConfigDict
 
 
-class SVEndpointHandler:
-    """
-    SambaNova Systems Interface for Sambaverse endpoint.
-
-    :param str host_url: Base URL of the DaaS API service
-    """
-
-    API_BASE_PATH: str = "/api/predict"
-
-    def __init__(self, host_url: str):
-        """
-        Initialize the SVEndpointHandler.
-
-        :param str host_url: Base URL of the DaaS API service
-        """
-        self.host_url = host_url
-        self.http_session = requests.Session()
-
-    @staticmethod
-    def _process_response(response: requests.Response) -> Dict:
-        """
-        Processes the API response and returns the resulting dict.
-
-        All resulting dicts, regardless of success or failure, will contain the
-        `status_code` key with the API response status code.
-
-        If the API returned an error, the resulting dict will contain the key
-        `detail` with the error message.
-
-        If the API call was successful, the resulting dict will contain the key
-        `data` with the response data.
-
-        :param requests.Response response: the response object to process
-        :return: the response dict
-        :type: dict
-        """
-        result: Dict[str, Any] = {}
-        try:
-            lines_result = response.text.strip().split("\n")
-            text_result = lines_result[-1]
-            if response.status_code == 200 and json.loads(text_result).get("error"):
-                completion = ""
-                for line in lines_result[:-1]:
-                    completion += json.loads(line)["result"]["responses"][0][
-                        "stream_token"
-                    ]
-                text_result = lines_result[-2]
-                result = json.loads(text_result)
-                result["result"]["responses"][0]["completion"] = completion
-            else:
-                result = json.loads(text_result)
-        except Exception as e:
-            result["detail"] = str(e)
-        if "status_code" not in result:
-            result["status_code"] = response.status_code
-        return result
-
-    @staticmethod
-    def _process_streaming_response(
-        response: requests.Response,
-    ) -> Generator[Dict, None, None]:
-        """Process the streaming response"""
-        try:
-            for line in response.iter_lines():
-                chunk = json.loads(line)
-                if "status_code" not in chunk:
-                    chunk["status_code"] = response.status_code
-                if chunk["status_code"] == 200 and chunk.get("error"):
-                    chunk["result"] = {"responses": [{"stream_token": ""}]}
-                    return chunk
-                yield chunk
-        except Exception as e:
-            raise RuntimeError(f"Error processing streaming response: {e}")
-
-    def _get_full_url(self) -> str:
-        """
-        Return the full API URL for a given path.
-        :returns: the full API URL for the sub-path
-        :type: str
-        """
-        return f"{self.host_url}{self.API_BASE_PATH}"
-
-    def nlp_predict(
-        self,
-        key: str,
-        sambaverse_model_name: Optional[str],
-        input: Union[List[str], str],
-        params: Optional[str] = "",
-        stream: bool = False,
-    ) -> Dict:
-        """
-        NLP predict using inline input string.
-
-        :param str project: Project ID in which the endpoint exists
-        :param str endpoint: Endpoint ID
-        :param str key: API Key
-        :param str input_str: Input string
-        :param str params: Input params string
-        :returns: Prediction results
-        :type: dict
-        """
-        if params:
-            data = {"instance": input, "params": json.loads(params)}
-        else:
-            data = {"instance": input}
-        response = self.http_session.post(
-            self._get_full_url(),
-            headers={
-                "key": key,
-                "Content-Type": "application/json",
-                "modelName": sambaverse_model_name,
-            },
-            json=data,
-        )
-        return SVEndpointHandler._process_response(response)
-
-    def nlp_predict_stream(
-        self,
-        key: str,
-        sambaverse_model_name: Optional[str],
-        input: Union[List[str], str],
-        params: Optional[str] = "",
-    ) -> Iterator[Dict]:
-        """
-        NLP predict using inline input string.
-
-        :param str project: Project ID in which the endpoint exists
-        :param str endpoint: Endpoint ID
-        :param str key: API Key
-        :param str input_str: Input string
-        :param str params: Input params string
-        :returns: Prediction results
-        :type: dict
-        """
-        if params:
-            data = {"instance": input, "params": json.loads(params)}
-        else:
-            data = {"instance": input}
-        # Streaming output
-        response = self.http_session.post(
-            self._get_full_url(),
-            headers={
-                "key": key,
-                "Content-Type": "application/json",
-                "modelName": sambaverse_model_name,
-            },
-            json=data,
-            stream=True,
-        )
-        for chunk in SVEndpointHandler._process_streaming_response(response):
-            yield chunk
-
-
-class Sambaverse(LLM):
-    """
-    Sambaverse large language models.
-
-    To use, you should have the environment variable ``SAMBAVERSE_API_KEY``
-    set with your API key.
-
-    get one in https://sambaverse.sambanova.ai
-    read extra documentation in https://docs.sambanova.ai/sambaverse/latest/index.html
-
-
-    Example:
-    .. code-block:: python
-
-        from langchain_community.llms.sambanova  import Sambaverse
-        Sambaverse(
-            sambaverse_url="https://sambaverse.sambanova.ai",
-            sambaverse_api_key="your-sambaverse-api-key",
-            sambaverse_model_name="Meta/llama-2-7b-chat-hf",
-            streaming: = False
-            model_kwargs={
-                "select_expert": "llama-2-7b-chat-hf",
-                "do_sample": False,
-                "max_tokens_to_generate": 100,
-                "temperature": 0.7,
-                "top_p": 1.0,
-                "repetition_penalty": 1.0,
-                "top_k": 50,
-                "process_prompt": False
-            },
-        )
-    """
-
-    sambaverse_url: str = ""
-    """Sambaverse url to use"""
-
-    sambaverse_api_key: str = ""
-    """sambaverse api key"""
-
-    sambaverse_model_name: Optional[str] = None
-    """sambaverse expert model to use"""
-
-    model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
-
-    streaming: Optional[bool] = False
-    """Streaming flag to get streamed response."""
-
-    model_config = ConfigDict(
-        extra="forbid",
-    )
-
-    @classmethod
-    def is_lc_serializable(cls) -> bool:
-        return True
-
-    @pre_init
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that api key exists in environment."""
-        values["sambaverse_url"] = get_from_dict_or_env(
-            values,
-            "sambaverse_url",
-            "SAMBAVERSE_URL",
-            default="https://sambaverse.sambanova.ai",
-        )
-        values["sambaverse_api_key"] = get_from_dict_or_env(
-            values, "sambaverse_api_key", "SAMBAVERSE_API_KEY"
-        )
-        values["sambaverse_model_name"] = get_from_dict_or_env(
-            values, "sambaverse_model_name", "SAMBAVERSE_MODEL_NAME"
-        )
-        return values
-
-    @property
-    def _identifying_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
-        return {**{"model_kwargs": self.model_kwargs}}
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "Sambaverse LLM"
-
-    def _get_tuning_params(self, stop: Optional[List[str]]) -> str:
-        """
-        Get the tuning parameters to use when calling the LLM.
-
-        Args:
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-
-        Returns:
-            The tuning parameters as a JSON string.
-        """
-        _model_kwargs = self.model_kwargs or {}
-        _kwarg_stop_sequences = _model_kwargs.get("stop_sequences", [])
-        _stop_sequences = stop or _kwarg_stop_sequences
-        if not _kwarg_stop_sequences:
-            _model_kwargs["stop_sequences"] = ",".join(
-                f'"{x}"' for x in _stop_sequences
-            )
-        tuning_params_dict = {
-            k: {"type": type(v).__name__, "value": str(v)}
-            for k, v in (_model_kwargs.items())
-        }
-        _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
-        tuning_params = json.dumps(tuning_params_dict)
-        return tuning_params
-
-    def _handle_nlp_predict(
-        self,
-        sdk: SVEndpointHandler,
-        prompt: Union[List[str], str],
-        tuning_params: str,
-    ) -> str:
-        """
-        Perform an NLP prediction using the Sambaverse endpoint handler.
-
-        Args:
-            sdk: The SVEndpointHandler to use for the prediction.
-            prompt: The prompt to use for the prediction.
-            tuning_params: The tuning parameters to use for the prediction.
-
-        Returns:
-            The prediction result.
-
-        Raises:
-            ValueError: If the prediction fails.
-        """
-        response = sdk.nlp_predict(
-            self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
-        )
-        if response["status_code"] != 200:
-            error = response.get("error")
-            if error:
-                optional_code = error.get("code")
-                optional_details = error.get("details")
-                optional_message = error.get("message")
-                raise RuntimeError(
-                    f"Sambanova /complete call failed with status code "
-                    f"{response['status_code']}.\n"
-                    f"Message: {optional_message}\n"
-                    f"Details: {optional_details}\n"
-                    f"Code: {optional_code}\n"
-                )
-            else:
-                raise RuntimeError(
-                    f"Sambanova /complete call failed with status code "
-                    f"{response['status_code']}."
-                    f"{response}."
-                )
-        return response["result"]["responses"][0]["completion"]
-
-    def _handle_completion_requests(
-        self, prompt: Union[List[str], str], stop: Optional[List[str]]
-    ) -> str:
-        """
-        Perform a prediction using the Sambaverse endpoint handler.
-
-        Args:
-            prompt: The prompt to use for the prediction.
-            stop: stop sequences.
-
-        Returns:
-            The prediction result.
-
-        Raises:
-            ValueError: If the prediction fails.
-        """
-        ss_endpoint = SVEndpointHandler(self.sambaverse_url)
-        tuning_params = self._get_tuning_params(stop)
-        return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)
-
-    def _handle_nlp_predict_stream(
-        self, sdk: SVEndpointHandler, prompt: Union[List[str], str], tuning_params: str
-    ) -> Iterator[GenerationChunk]:
-        """
-        Perform a streaming request to the LLM.
-
-        Args:
-            sdk: The SVEndpointHandler to use for the prediction.
-            prompt: The prompt to use for the prediction.
-            tuning_params: The tuning parameters to use for the prediction.
-
-        Returns:
-            An iterator of GenerationChunks.
-        """
-        for chunk in sdk.nlp_predict_stream(
-            self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
-        ):
-            if chunk["status_code"] != 200:
-                error = chunk.get("error")
-                if error:
-                    optional_code = error.get("code")
-                    optional_details = error.get("details")
-                    optional_message = error.get("message")
-                    raise ValueError(
-                        f"Sambanova /complete call failed with status code "
-                        f"{chunk['status_code']}.\n"
-                        f"Message: {optional_message}\n"
-                        f"Details: {optional_details}\n"
-                        f"Code: {optional_code}\n"
-                    )
-                else:
-                    raise RuntimeError(
-                        f"Sambanova /complete call failed with status code "
-                        f"{chunk['status_code']}."
-                        f"{chunk}."
-                    )
-            text = chunk["result"]["responses"][0]["stream_token"]
-            generated_chunk = GenerationChunk(text=text)
-            yield generated_chunk
-
-    def _stream(
-        self,
-        prompt: Union[List[str], str],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> Iterator[GenerationChunk]:
-        """Stream the Sambaverse's LLM on the given prompt.
-
-        Args:
-            prompt: The prompt to pass into the model.
-            stop: Optional list of stop words to use when generating.
-            run_manager: Callback manager for the run.
-            kwargs: Additional keyword arguments. directly passed
-                to the sambaverse model in API call.
-
-        Returns:
-            An iterator of GenerationChunks.
-        """
-        ss_endpoint = SVEndpointHandler(self.sambaverse_url)
-        tuning_params = self._get_tuning_params(stop)
-        try:
-            if self.streaming:
-                for chunk in self._handle_nlp_predict_stream(
-                    ss_endpoint, prompt, tuning_params
-                ):
-                    if run_manager:
-                        run_manager.on_llm_new_token(chunk.text)
-                    yield chunk
-            else:
-                return
-        except Exception as e:
-            # Handle any errors raised by the inference endpoint
-            raise ValueError(f"Error raised by the inference endpoint: {e}") from e
-
-    def _handle_stream_request(
-        self,
-        prompt: Union[List[str], str],
-        stop: Optional[List[str]],
-        run_manager: Optional[CallbackManagerForLLMRun],
-        kwargs: Dict[str, Any],
-    ) -> str:
-        """
-        Perform a streaming request to the LLM.
-
-        Args:
-            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-            run_manager: Callback manager for the run.
-            kwargs: Additional keyword arguments. directly passed
-                to the sambaverse model in API call.
-
-        Returns:
-            The model output as a string.
-        """
-        completion = ""
-        for chunk in self._stream(
-            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
-        ):
-            completion += chunk.text
-        return completion
-
-    def _call(
-        self,
-        prompt: Union[List[str], str],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> str:
-        """Run the LLM on the given input.
-
-        Args:
-            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-            run_manager: Callback manager for the run.
-            kwargs: Additional keyword arguments. directly passed
-                to the sambaverse model in API call.
-
-        Returns:
-            The model output as a string.
-        """
-        try:
-            if self.streaming:
-                return self._handle_stream_request(prompt, stop, run_manager, kwargs)
-            return self._handle_completion_requests(prompt, stop)
-        except Exception as e:
-            # Handle any errors raised by the inference endpoint
-            raise ValueError(f"Error raised by the inference endpoint: {e}") from e
-
-
 class SSEndpointHandler:
     """
     SambaNova Systems Interface for SambaStudio model endpoints.
@@ -975,7 +517,7 @@ class SambaStudio(LLM):
                 first occurrence of any of the stop substrings.
             run_manager: Callback manager for the run.
             kwargs: Additional keyword arguments. directly passed
-                to the sambaverse model in API call.
+                to the sambastudio model in API call.
 
         Returns:
             The model output as a string.

libs/community/langchain_community/tools/databricks/tool.py

@@ -10,7 +10,6 @@ from pydantic import BaseModel, Field, create_model
 from typing_extensions import Self
 
 if TYPE_CHECKING:
-    from databricks.sdk import WorkspaceClient
     from databricks.sdk.service.catalog import FunctionInfo
 
 from pydantic import ConfigDict
@@ -121,7 +120,7 @@ def _get_tool_name(function: "FunctionInfo") -> str:
     return tool_name
 
 
-def _get_default_workspace_client() -> "WorkspaceClient":
+def _get_default_workspace_client() -> Any:
     try:
         from databricks.sdk import WorkspaceClient
     except ImportError as e:
@@ -137,7 +136,7 @@ class UCFunctionToolkit(BaseToolkit):
         description="The ID of a Databricks SQL Warehouse to execute functions."
     )
 
-    workspace_client: "WorkspaceClient" = Field(
+    workspace_client: Any = Field(
         default_factory=_get_default_workspace_client,
         description="Databricks workspace client.",
     )

libs/community/langchain_community/vectorstores/azuresearch.py

@@ -443,6 +443,12 @@ class AzureSearch(VectorStore):
             logger.debug("Nothing to insert, skipping.")
             return []
 
+        # when `keys` are not passed in and there is `ids` in kwargs, use those instead
+        # base class expects `ids` passed in rather than `keys`
+        # https://github.com/langchain-ai/langchain/blob/4cdaca67dc51dba887289f56c6fead3c1a52f97d/libs/core/langchain_core/vectorstores/base.py#L65
+        if (not keys) and ("ids" in kwargs) and (len(kwargs["ids"]) == len(embeddings)):
+            keys = kwargs["ids"]
+
         return self.add_embeddings(zip(texts, embeddings), metadatas, keys=keys)
 
     async def aadd_texts(
@@ -467,6 +473,12 @@ class AzureSearch(VectorStore):
             logger.debug("Nothing to insert, skipping.")
             return []
 
+        # when `keys` are not passed in and there is `ids` in kwargs, use those instead
+        # base class expects `ids` passed in rather than `keys`
+        # https://github.com/langchain-ai/langchain/blob/4cdaca67dc51dba887289f56c6fead3c1a52f97d/libs/core/langchain_core/vectorstores/base.py#L65
+        if (not keys) and ("ids" in kwargs) and (len(kwargs["ids"]) == len(embeddings)):
+            keys = kwargs["ids"]
+
         return await self.aadd_embeddings(zip(texts, embeddings), metadatas, keys=keys)
 
     def add_embeddings(
@@ -483,9 +495,13 @@ class AzureSearch(VectorStore):
         data = []
         for i, (text, embedding) in enumerate(text_embeddings):
             # Use provided key otherwise use default key
-            key = keys[i] if keys else str(uuid.uuid4())
-            # Encoding key for Azure Search valid characters
-            key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
+            if keys:
+                key = keys[i]
+            else:
+                key = str(uuid.uuid4())
+                # Encoding key for Azure Search valid characters
+                key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
+
             metadata = metadatas[i] if metadatas else {}
             # Add data to index
             # Additional metadata to fields mapping

libs/community/langchain_community/vectorstores/neo4j_vector.py

@@ -595,11 +595,8 @@ class Neo4jVector(VectorStore):
         query: str,
         *,
         params: Optional[dict] = None,
-        retry_on_session_expired: bool = True,
     ) -> List[Dict[str, Any]]:
-        """
-        This method sends a Cypher query to the connected Neo4j database
-        and returns the results as a list of dictionaries.
+        """Query Neo4j database with retries and exponential backoff.
 
         Args:
             query (str): The Cypher query to execute.
@@ -608,24 +605,38 @@ class Neo4jVector(VectorStore):
         Returns:
             List[Dict[str, Any]]: List of dictionaries containing the query results.
         """
-        from neo4j.exceptions import CypherSyntaxError, SessionExpired
+        from neo4j import Query
+        from neo4j.exceptions import Neo4jError
 
         params = params or {}
-        with self._driver.session(database=self._database) as session:
-            try:
-                data = session.run(query, params)
-                return [r.data() for r in data]
-            except CypherSyntaxError as e:
-                raise ValueError(f"Cypher Statement is not valid\n{e}")
-            except (
-                SessionExpired
-            ) as e:  # Session expired is a transient error that can be retried
-                if retry_on_session_expired:
-                    return self.query(
-                        query, params=params, retry_on_session_expired=False
+        try:
+            data, _, _ = self._driver.execute_query(
+                query, database=self._database, parameters_=params
+            )
+            return [r.data() for r in data]
+        except Neo4jError as e:
+            if not (
+                (
+                    (  # isCallInTransactionError
+                        e.code == "Neo.DatabaseError.Statement.ExecutionFailed"
+                        or e.code
+                        == "Neo.DatabaseError.Transaction.TransactionStartFailed"
                     )
-                else:
-                    raise e
+                    and "in an implicit transaction" in e.message
+                )
+                or (  # isPeriodicCommitError
+                    e.code == "Neo.ClientError.Statement.SemanticError"
+                    and (
+                        "in an open transaction is not possible" in e.message
+                        or "tried to execute in an explicit transaction" in e.message
+                    )
+                )
+            ):
+                raise
+        # Fallback to allow implicit transactions
+        with self._driver.session() as session:
+            data = session.run(Query(text=query), params)
+            return [r.data() for r in data]
 
     def verify_version(self) -> None:
         """

libs/community/langchain_community/vectorstores/tencentvectordb.py

@@ -144,7 +144,7 @@ class TencentVectorDB(VectorStore):
 
     In order to use this you need to have a database instance.
     See the following documentation for details:
-    https://cloud.tencent.com/document/product/1709/94951
+    https://cloud.tencent.com/document/product/1709/104489
     """
 
     field_id: str = "id"

libs/community/scripts/check_pydantic.sh

@@ -20,7 +20,7 @@ count=$(git grep -E '(@root_validator)|(@validator)|(@field_validator)|(@pre_ini
 # PRs that increase the current count will not be accepted.
 # PRs that decrease update the code in the repository
 # and allow decreasing the count of are welcome!
-current_count=129
+current_count=128
 
 if [ "$count" -gt "$current_count" ]; then
   echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator."

libs/community/tests/integration_tests/document_loaders/test_pdf.py

@@ -1,4 +1,3 @@
-import re
 from pathlib import Path
 from typing import Sequence, Union
 
@@ -11,7 +10,6 @@ from langchain_community.document_loaders import (
     PDFMinerPDFasHTMLLoader,
     PyMuPDFLoader,
     PyPDFium2Loader,
-    PyPDFLoader,
     UnstructuredPDFLoader,
 )
 
@@ -86,37 +84,6 @@ def test_pdfminer_pdf_as_html_loader() -> None:
     assert len(docs) == 1
 
 
-def test_pypdf_loader() -> None:
-    """Test PyPDFLoader."""
-    file_path = Path(__file__).parent.parent / "examples/hello.pdf"
-    loader = PyPDFLoader(str(file_path))
-    docs = loader.load()
-
-    assert len(docs) == 1
-
-    file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
-    loader = PyPDFLoader(str(file_path))
-
-    docs = loader.load()
-    assert len(docs) == 16
-
-
-def test_pypdf_loader_with_layout() -> None:
-    """Test PyPDFLoader with layout mode."""
-    file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
-    loader = PyPDFLoader(str(file_path), extraction_mode="layout")
-
-    docs = loader.load()
-    first_page = docs[0].page_content
-
-    expected = (
-        Path(__file__).parent.parent / "examples/layout-parser-paper-page-1.txt"
-    ).read_text(encoding="utf-8")
-    cleaned_first_page = re.sub(r"\x00", "", first_page)
-    cleaned_expected = re.sub(r"\x00", "", expected)
-    assert cleaned_first_page == cleaned_expected
-
-
 def test_pypdfium2_loader() -> None:
     """Test PyPDFium2Loader."""
     file_path = Path(__file__).parent.parent / "examples/hello.pdf"

libs/community/tests/integration_tests/graph_vectorstores/extractors/test_gliner_link_extractor.py

@@ -1,7 +1,7 @@
 import pytest
-from langchain_core.graph_vectorstores.links import Link
 
 from langchain_community.graph_vectorstores.extractors import GLiNERLinkExtractor
+from langchain_community.graph_vectorstores.links import Link
 
 PAGE_1 = """
 Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃ'tjɐnu

libs/community/tests/integration_tests/graph_vectorstores/extractors/test_keybert_link_extractor.py

@@ -1,7 +1,7 @@
 import pytest
-from langchain_core.graph_vectorstores.links import Link
 
 from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
+from langchain_community.graph_vectorstores.links import Link
 
 PAGE_1 = """
 Supervised learning is the machine learning task of learning a function that

libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py

@@ -4,9 +4,9 @@ from typing import Iterable, List, Optional, Type
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.graph_vectorstores.links import METADATA_LINKS_KEY, Link
 
 from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
 
 CASSANDRA_DEFAULT_KEYSPACE = "graph_test_keyspace"
 

libs/community/tests/integration_tests/llms/test_sambanova.py

@@ -1,28 +1,17 @@
 """Test sambanova API wrapper.
 
-In order to run this test, you need to have an sambaverse api key,
-and a sambaverse base url, project id, endpoint id, and api key.
-You'll then need to set SAMBAVERSE_API_KEY, SAMBASTUDIO_BASE_URL,
+In order to run this test, you need to have a sambastudio base url,
+project id, endpoint id, and api key.
+You'll then need to set SAMBASTUDIO_BASE_URL, SAMBASTUDIO_BASE_URI
 SAMBASTUDIO_PROJECT_ID, SAMBASTUDIO_ENDPOINT_ID, and SAMBASTUDIO_API_KEY
 environment variables.
 """
 
-from langchain_community.llms.sambanova import SambaStudio, Sambaverse
-
-
-def test_sambaverse_call() -> None:
-    """Test simple non-streaming call to sambaverse."""
-    llm = Sambaverse(
-        sambaverse_model_name="Meta/llama-2-7b-chat-hf",
-        model_kwargs={"select_expert": "llama-2-7b-chat-hf"},
-    )
-    output = llm.invoke("What is LangChain")
-    assert output
-    assert isinstance(output, str)
+from langchain_community.llms.sambanova import SambaStudio
 
 
 def test_sambastudio_call() -> None:
-    """Test simple non-streaming call to sambaverse."""
+    """Test simple non-streaming call to sambastudio."""
     llm = SambaStudio()
     output = llm.invoke("What is LangChain")
     assert output

libs/community/tests/unit_tests/callbacks/test_callback_manager.py

@@ -121,4 +121,4 @@ def test_callback_manager_configure_context_vars(
                     assert cb.completion_tokens == 1
                     assert cb.total_cost > 0
             wait_for_all_tracers()
-            assert LangChainTracer._persist_run_single.call_count == 1  # type: ignore
+            assert LangChainTracer._persist_run_single.call_count == 4  # type: ignore

libs/community/tests/unit_tests/document_loaders/test_imports.py

@@ -55,6 +55,7 @@ EXPECTED_ALL = [
     "DedocFileLoader",
     "DedocPDFLoader",
     "PebbloSafeLoader",
+    "PebbloTextLoader",
     "DiffbotLoader",
     "DirectoryLoader",
     "DiscordChatLoader",

libs/community/tests/unit_tests/document_loaders/test_mongodb.py

@@ -12,6 +12,7 @@ def raw_docs() -> List[Dict]:
     return [
         {"_id": "1", "address": {"building": "1", "room": "1"}},
         {"_id": "2", "address": {"building": "2", "room": "2"}},
+        {"_id": "3", "address": {"building": "3", "room": "2"}},
     ]
 
 
@@ -19,18 +20,23 @@ def raw_docs() -> List[Dict]:
 def expected_documents() -> List[Document]:
     return [
         Document(
-            page_content="{'_id': '1', 'address': {'building': '1', 'room': '1'}}",
+            page_content="{'_id': '2', 'address': {'building': '2', 'room': '2'}}",
             metadata={"database": "sample_restaurants", "collection": "restaurants"},
         ),
         Document(
-            page_content="{'_id': '2', 'address': {'building': '2', 'room': '2'}}",
+            page_content="{'_id': '3', 'address': {'building': '3', 'room': '2'}}",
             metadata={"database": "sample_restaurants", "collection": "restaurants"},
         ),
     ]
 
 
 @pytest.mark.requires("motor")
-async def test_load_mocked(expected_documents: List[Document]) -> None:
+async def test_load_mocked_with_filters(expected_documents: List[Document]) -> None:
+    filter_criteria = {"address.room": {"$eq": "2"}}
+    field_names = ["address.building", "address.room"]
+    metadata_names = ["_id"]
+    include_db_collection_in_metadata = True
+
     mock_async_load = AsyncMock()
     mock_async_load.return_value = expected_documents
 
@@ -51,7 +57,13 @@ async def test_load_mocked(expected_documents: List[Document]) -> None:
         new=mock_async_load,
     ):
         loader = MongodbLoader(
-            "mongodb://localhost:27017", "test_db", "test_collection"
+            "mongodb://localhost:27017",
+            "test_db",
+            "test_collection",
+            filter_criteria=filter_criteria,
+            field_names=field_names,
+            metadata_names=metadata_names,
+            include_db_collection_in_metadata=include_db_collection_in_metadata,
         )
         loader.collection = mock_collection
         documents = await loader.aload()

libs/community/tests/unit_tests/document_loaders/test_pdf.py

@@ -0,0 +1,62 @@
+import re
+from pathlib import Path
+
+import pytest
+
+from langchain_community.document_loaders import PyPDFLoader
+
+path_to_simple_pdf = (
+    Path(__file__).parent.parent.parent / "integration_tests/examples/hello.pdf"
+)
+path_to_layout_pdf = (
+    Path(__file__).parent.parent
+    / "document_loaders/sample_documents/layout-parser-paper.pdf"
+)
+path_to_layout_pdf_txt = (
+    Path(__file__).parent.parent.parent
+    / "integration_tests/examples/layout-parser-paper-page-1.txt"
+)
+
+
+@pytest.mark.requires("pypdf")
+def test_pypdf_loader() -> None:
+    """Test PyPDFLoader."""
+    loader = PyPDFLoader(str(path_to_simple_pdf))
+    docs = loader.load()
+
+    assert len(docs) == 1
+
+    loader = PyPDFLoader(str(path_to_layout_pdf))
+
+    docs = loader.load()
+    assert len(docs) == 16
+    for page, doc in enumerate(docs):
+        assert doc.metadata["page"] == page
+        assert doc.metadata["source"].endswith("layout-parser-paper.pdf")
+        assert len(doc.page_content) > 10
+
+    first_page = docs[0].page_content
+    for expected in ["LayoutParser", "A Unified Toolkit"]:
+        assert expected in first_page
+
+
+@pytest.mark.requires("pypdf")
+def test_pypdf_loader_with_layout() -> None:
+    """Test PyPDFLoader with layout mode."""
+    loader = PyPDFLoader(str(path_to_layout_pdf), extraction_mode="layout")
+
+    docs = loader.load()
+    assert len(docs) == 16
+    for page, doc in enumerate(docs):
+        assert doc.metadata["page"] == page
+        assert doc.metadata["source"].endswith("layout-parser-paper.pdf")
+        assert len(doc.page_content) > 10
+
+    first_page = docs[0].page_content
+    for expected in ["LayoutParser", "A Unified Toolkit"]:
+        assert expected in first_page
+
+    expected = path_to_layout_pdf_txt.read_text(encoding="utf-8")
+    cleaned_first_page = re.sub(r"\x00", "", first_page)
+    cleaned_expected = re.sub(r"\x00", "", expected)
+    assert cleaned_first_page == cleaned_expected

libs/community/tests/unit_tests/document_loaders/test_pebblo.py

@@ -25,6 +25,11 @@ def test_pebblo_import() -> None:
     from langchain_community.document_loaders import PebbloSafeLoader  # noqa: F401
 
 
+def test_pebblo_text_loader_import() -> None:
+    """Test that the Pebblo text loader can be imported."""
+    from langchain_community.document_loaders import PebbloTextLoader  # noqa: F401
+
+
 def test_empty_filebased_loader(mocker: MockerFixture) -> None:
     """Test basic file based csv loader."""
     # Setup
@@ -146,3 +151,42 @@ def test_pebblo_safe_loader_api_key() -> None:
     # Assert
     assert loader.pb_client.api_key == api_key
     assert loader.pb_client.classifier_location == "local"
+
+
+def test_pebblo_text_loader(mocker: MockerFixture) -> None:
+    """
+    Test loading in-memory text with PebbloTextLoader and PebbloSafeLoader.
+    """
+    # Setup
+    from langchain_community.document_loaders import PebbloSafeLoader, PebbloTextLoader
+
+    mocker.patch.multiple(
+        "requests",
+        get=MockResponse(json_data={"data": ""}, status_code=200),
+        post=MockResponse(json_data={"data": ""}, status_code=200),
+    )
+
+    text = "This is a test text."
+    source = "fake_source"
+    expected_docs = [
+        Document(
+            metadata={
+                "full_path": source,
+                "pb_checksum": None,
+            },
+            page_content=text,
+        ),
+    ]
+
+    # Exercise
+    texts = [text]
+    loader = PebbloSafeLoader(
+        PebbloTextLoader(texts, source=source),
+        "dummy_app_name",
+        "dummy_owner",
+        "dummy_description",
+    )
+    result = loader.load()
+
+    # Assert
+    assert result == expected_docs

libs/community/tests/unit_tests/graph_vectorstores/extractors/test_hierarchy_link_extractor.py

@@ -1,6 +1,5 @@
-from langchain_core.graph_vectorstores.links import Link
-
 from langchain_community.graph_vectorstores.extractors import HierarchyLinkExtractor
+from langchain_community.graph_vectorstores.links import Link
 
 PATH_1 = ["Root", "H1", "h2"]
 

libs/community/tests/unit_tests/graph_vectorstores/extractors/test_html_link_extractor.py

@@ -1,6 +1,6 @@
 import pytest
-from langchain_core.graph_vectorstores import Link
 
+from langchain_community.graph_vectorstores import Link
 from langchain_community.graph_vectorstores.extractors import (
     HtmlInput,
     HtmlLinkExtractor,

libs/community/tests/unit_tests/graph_vectorstores/extractors/test_link_extractor_transformer.py

@@ -1,12 +1,12 @@
 from typing import Set
 
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.links import Link, get_links
 
 from langchain_community.graph_vectorstores.extractors import (
     LinkExtractor,
     LinkExtractorTransformer,
 )
+from langchain_community.graph_vectorstores.links import Link, get_links
 
 TEXT1 = "Text1"
 TEXT2 = "Text2"

libs/community/tests/unit_tests/llms/test_imports.py

@@ -77,7 +77,6 @@ EXPECT_ALL = [
     "RWKV",
     "Replicate",
     "SagemakerEndpoint",
-    "Sambaverse",
     "SambaStudio",
     "SelfHostedHuggingFaceLLM",
     "SelfHostedPipeline",

libs/community/tests/unit_tests/test_graph_vectorstores.py

@@ -1,12 +1,12 @@
 import pytest
-
 from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.base import (
+
+from langchain_community.graph_vectorstores.base import (
     Node,
     _documents_to_nodes,
     _texts_to_nodes,
 )
-from langchain_core.graph_vectorstores.links import Link
+from langchain_community.graph_vectorstores.links import Link
 
 
 def test_texts_to_nodes() -> None:

libs/community/tests/unit_tests/vectorstores/test_azure_search.py

@@ -190,3 +190,40 @@ def test_additional_search_options() -> None:
         )
         assert vector_store.client is not None
         assert vector_store.client._api_version == "test"
+
+
+@pytest.mark.requires("azure.search.documents")
+def test_ids_used_correctly() -> None:
+    """Check whether vector store uses the document ids when provided with them."""
+    from azure.search.documents import SearchClient
+    from azure.search.documents.indexes import SearchIndexClient
+    from langchain_core.documents import Document
+
+    class Response:
+        def __init__(self) -> None:
+            self.succeeded: bool = True
+
+    def mock_upload_documents(self, documents: List[object]) -> List[Response]:  # type: ignore[no-untyped-def]
+        # assume all documents uploaded successfuly
+        response = [Response() for _ in documents]
+        return response
+
+    documents = [
+        Document(
+            page_content="page zero Lorem Ipsum",
+            metadata={"source": "document.pdf", "page": 0, "id": "ID-document-1"},
+        ),
+        Document(
+            page_content="page one Lorem Ipsum",
+            metadata={"source": "document.pdf", "page": 1, "id": "ID-document-2"},
+        ),
+    ]
+    ids_provided = [i.metadata.get("id") for i in documents]
+
+    with patch.object(
+        SearchClient, "upload_documents", mock_upload_documents
+    ), patch.object(SearchIndexClient, "get_index", mock_default_index):
+        vector_store = create_vector_store()
+        ids_used_at_upload = vector_store.add_documents(documents, ids=ids_provided)
+        assert len(ids_provided) == len(ids_used_at_upload)
+        assert ids_provided == ids_used_at_upload

libs/core/README.md

@@ -53,7 +53,7 @@ LangChain Core compiles LCEL sequences to an _optimized execution plan_, with au
 
 For more check out the [LCEL docs](https://python.langchain.com/docs/expression_language/).
 
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](../../docs/static/svg/langchain_stack_062024.svg "LangChain Framework Overview")
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](https://raw.githubusercontent.com/langchain-ai/langchain/e1d113ea84a2edcf4a7709fc5be0e972ea74a5d9/docs/static/svg/langchain_stack_062024.svg "LangChain Framework Overview")
 
 For more advanced use cases, also check out [LangGraph](https://github.com/langchain-ai/langgraph), which is a graph-based runner for cyclic and recursive LLM workflows.
 

libs/core/langchain_core/_api/beta_decorator.py

@@ -14,7 +14,8 @@ import contextlib
 import functools
 import inspect
 import warnings
-from typing import Any, Callable, Generator, Type, TypeVar, Union, cast
+from collections.abc import Generator
+from typing import Any, Callable, TypeVar, Union, cast
 
 from langchain_core._api.internal import is_caller_internal
 
@@ -26,7 +27,7 @@ class LangChainBetaWarning(DeprecationWarning):
 # PUBLIC API
 
 
-T = TypeVar("T", bound=Union[Callable[..., Any], Type])
+T = TypeVar("T", bound=Union[Callable[..., Any], type])
 
 
 def beta(
@@ -154,7 +155,7 @@ def beta(
             _name = _name or obj.fget.__qualname__
             old_doc = obj.__doc__
 
-            class _beta_property(property):
+            class _BetaProperty(property):
                 """A beta property."""
 
                 def __init__(self, fget=None, fset=None, fdel=None, doc=None):
@@ -185,7 +186,7 @@ def beta(
 
             def finalize(wrapper: Callable[..., Any], new_doc: str) -> Any:
                 """Finalize the property."""
-                return _beta_property(
+                return _BetaProperty(
                     fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
                 )
 

libs/core/langchain_core/_api/deprecation.py

@@ -14,11 +14,10 @@ import contextlib
 import functools
 import inspect
 import warnings
+from collections.abc import Generator
 from typing import (
     Any,
     Callable,
-    Generator,
-    Type,
     TypeVar,
     Union,
     cast,
@@ -41,7 +40,7 @@ class LangChainPendingDeprecationWarning(PendingDeprecationWarning):
 
 
 # Last Any should be FieldInfoV1 but this leads to circular imports
-T = TypeVar("T", bound=Union[Type, Callable[..., Any], Any])
+T = TypeVar("T", bound=Union[type, Callable[..., Any], Any])
 
 
 def _validate_deprecation_params(
@@ -262,10 +261,10 @@ def deprecated(
             if not _obj_type:
                 _obj_type = "attribute"
             wrapped = None
-            _name = _name or cast(Union[Type, Callable], obj.fget).__qualname__
+            _name = _name or cast(Union[type, Callable], obj.fget).__qualname__
             old_doc = obj.__doc__
 
-            class _deprecated_property(property):
+            class _DeprecatedProperty(property):
                 """A deprecated property."""
 
                 def __init__(self, fget=None, fset=None, fdel=None, doc=None):  # type: ignore[no-untyped-def]
@@ -298,13 +297,13 @@ def deprecated(
                 """Finalize the property."""
                 return cast(
                     T,
-                    _deprecated_property(
+                    _DeprecatedProperty(
                         fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
                     ),
                 )
 
         else:
-            _name = _name or cast(Union[Type, Callable], obj).__qualname__
+            _name = _name or cast(Union[type, Callable], obj).__qualname__
             if not _obj_type:
                 # edge case: when a function is within another function
                 # within a test, this will call it a "method" not a "function"
@@ -333,9 +332,26 @@ def deprecated(
             old_doc = ""
 
         # Modify the docstring to include a deprecation notice.
+        if (
+            _alternative
+            and _alternative.split(".")[-1].lower() == _alternative.split(".")[-1]
+        ):
+            _alternative = f":meth:`~{_alternative}`"
+        elif _alternative:
+            _alternative = f":class:`~{_alternative}`"
+
+        if (
+            _alternative_import
+            and _alternative_import.split(".")[-1].lower()
+            == _alternative_import.split(".")[-1]
+        ):
+            _alternative_import = f":meth:`~{_alternative_import}`"
+        elif _alternative_import:
+            _alternative_import = f":class:`~{_alternative_import}`"
+
         components = [
             _message,
-            f"Use ``{_alternative}`` instead." if _alternative else "",
+            f"Use {_alternative} instead." if _alternative else "",
             f"Use ``{_alternative_import}`` instead." if _alternative_import else "",
             _addendum,
         ]

libs/core/langchain_core/agents.py

@@ -25,7 +25,8 @@ The schemas for the agents themselves are defined in langchain.agents.agent.
 from __future__ import annotations
 
 import json
-from typing import Any, List, Literal, Sequence, Union
+from collections.abc import Sequence
+from typing import Any, Literal, Union
 
 from langchain_core.load.serializable import Serializable
 from langchain_core.messages import (
@@ -71,7 +72,7 @@ class AgentAction(Serializable):
         return True
 
     @classmethod
-    def get_lc_namespace(cls) -> List[str]:
+    def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the langchain object.
         Default is ["langchain", "schema", "agent"]."""
         return ["langchain", "schema", "agent"]
@@ -145,7 +146,7 @@ class AgentFinish(Serializable):
         return True
 
     @classmethod
-    def get_lc_namespace(cls) -> List[str]:
+    def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the langchain object."""
         return ["langchain", "schema", "agent"]
 

libs/core/langchain_core/beta/runnables/context.py

@@ -1,19 +1,13 @@
 import asyncio
 import threading
 from collections import defaultdict
+from collections.abc import Awaitable, Mapping, Sequence
 from functools import partial
 from itertools import groupby
 from typing import (
     Any,
-    Awaitable,
     Callable,
-    DefaultDict,
-    Dict,
-    List,
-    Mapping,
     Optional,
-    Sequence,
-    Type,
     TypeVar,
     Union,
 )
@@ -30,7 +24,7 @@ from langchain_core.runnables.config import RunnableConfig, ensure_config, patch
 from langchain_core.runnables.utils import ConfigurableFieldSpec, Input, Output
 
 T = TypeVar("T")
-Values = Dict[Union[asyncio.Event, threading.Event], Any]
+Values = dict[Union[asyncio.Event, threading.Event], Any]
 CONTEXT_CONFIG_PREFIX = "__context__/"
 CONTEXT_CONFIG_SUFFIX_GET = "/get"
 CONTEXT_CONFIG_SUFFIX_SET = "/set"
@@ -70,10 +64,10 @@ def _key_from_id(id_: str) -> str:
 
 def _config_with_context(
     config: RunnableConfig,
-    steps: List[Runnable],
+    steps: list[Runnable],
     setter: Callable,
     getter: Callable,
-    event_cls: Union[Type[threading.Event], Type[asyncio.Event]],
+    event_cls: Union[type[threading.Event], type[asyncio.Event]],
 ) -> RunnableConfig:
     if any(k.startswith(CONTEXT_CONFIG_PREFIX) for k in config.get("configurable", {})):
         return config
@@ -99,10 +93,10 @@ def _config_with_context(
     }
 
     values: Values = {}
-    events: DefaultDict[str, Union[asyncio.Event, threading.Event]] = defaultdict(
+    events: defaultdict[str, Union[asyncio.Event, threading.Event]] = defaultdict(
         event_cls
     )
-    context_funcs: Dict[str, Callable[[], Any]] = {}
+    context_funcs: dict[str, Callable[[], Any]] = {}
     for key, group in grouped_by_key.items():
         getters = [s for s in group if s[0].id.endswith(CONTEXT_CONFIG_SUFFIX_GET)]
         setters = [s for s in group if s[0].id.endswith(CONTEXT_CONFIG_SUFFIX_SET)]
@@ -129,7 +123,7 @@ def _config_with_context(
 
 def aconfig_with_context(
     config: RunnableConfig,
-    steps: List[Runnable],
+    steps: list[Runnable],
 ) -> RunnableConfig:
     """Asynchronously patch a runnable config with context getters and setters.
 
@@ -145,7 +139,7 @@ def aconfig_with_context(
 
 def config_with_context(
     config: RunnableConfig,
-    steps: List[Runnable],
+    steps: list[Runnable],
 ) -> RunnableConfig:
     """Patch a runnable config with context getters and setters.
 
@@ -165,13 +159,13 @@ class ContextGet(RunnableSerializable):
 
     prefix: str = ""
 
-    key: Union[str, List[str]]
+    key: Union[str, list[str]]
 
     def __str__(self) -> str:
         return f"ContextGet({_print_keys(self.key)})"
 
     @property
-    def ids(self) -> List[str]:
+    def ids(self) -> list[str]:
         prefix = self.prefix + "/" if self.prefix else ""
         keys = self.key if isinstance(self.key, list) else [self.key]
         return [
@@ -180,7 +174,7 @@ class ContextGet(RunnableSerializable):
         ]
 
     @property
-    def config_specs(self) -> List[ConfigurableFieldSpec]:
+    def config_specs(self) -> list[ConfigurableFieldSpec]:
         return super().config_specs + [
             ConfigurableFieldSpec(
                 id=id_,
@@ -256,7 +250,7 @@ class ContextSet(RunnableSerializable):
         return f"ContextSet({_print_keys(list(self.keys.keys()))})"
 
     @property
-    def ids(self) -> List[str]:
+    def ids(self) -> list[str]:
         prefix = self.prefix + "/" if self.prefix else ""
         return [
             f"{CONTEXT_CONFIG_PREFIX}{prefix}{key}{CONTEXT_CONFIG_SUFFIX_SET}"
@@ -264,7 +258,7 @@ class ContextSet(RunnableSerializable):
         ]
 
     @property
-    def config_specs(self) -> List[ConfigurableFieldSpec]:
+    def config_specs(self) -> list[ConfigurableFieldSpec]:
         mapper_config_specs = [
             s
             for mapper in self.keys.values()
@@ -364,7 +358,7 @@ class Context:
         return PrefixContext(prefix=scope)
 
     @staticmethod
-    def getter(key: Union[str, List[str]], /) -> ContextGet:
+    def getter(key: Union[str, list[str]], /) -> ContextGet:
         return ContextGet(key=key)
 
     @staticmethod
@@ -385,7 +379,7 @@ class PrefixContext:
     def __init__(self, prefix: str = ""):
         self.prefix = prefix
 
-    def getter(self, key: Union[str, List[str]], /) -> ContextGet:
+    def getter(self, key: Union[str, list[str]], /) -> ContextGet:
         return ContextGet(key=key, prefix=self.prefix)
 
     def setter(

libs/core/langchain_core/caches.py

@@ -23,7 +23,8 @@ Cache directly competes with Memory. See documentation for Pros and Cons.
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, Sequence, Tuple
+from collections.abc import Sequence
+from typing import Any, Optional
 
 from langchain_core.outputs import Generation
 from langchain_core.runnables import run_in_executor
@@ -157,7 +158,7 @@ class InMemoryCache(BaseCache):
         Raises:
             ValueError: If maxsize is less than or equal to 0.
         """
-        self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {}
+        self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
         if maxsize is not None and maxsize <= 0:
             raise ValueError("maxsize must be greater than 0")
         self._maxsize = maxsize

libs/core/langchain_core/callbacks/base.py

@@ -3,7 +3,8 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, TypeVar, Union
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
 from uuid import UUID
 
 from tenacity import RetryCallState
@@ -118,7 +119,7 @@ class ChainManagerMixin:
 
     def on_chain_end(
         self,
-        outputs: Dict[str, Any],
+        outputs: dict[str, Any],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
@@ -222,13 +223,13 @@ class CallbackManagerMixin:
 
     def on_llm_start(
         self,
-        serialized: Dict[str, Any],
-        prompts: List[str],
+        serialized: dict[str, Any],
+        prompts: list[str],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when LLM starts running.
@@ -249,13 +250,13 @@ class CallbackManagerMixin:
 
     def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
-        messages: List[List[BaseMessage]],
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when a chat model starts running.
@@ -280,13 +281,13 @@ class CallbackManagerMixin:
 
     def on_retriever_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         query: str,
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when the Retriever starts running.
@@ -303,13 +304,13 @@ class CallbackManagerMixin:
 
     def on_chain_start(
         self,
-        serialized: Dict[str, Any],
-        inputs: Dict[str, Any],
+        serialized: dict[str, Any],
+        inputs: dict[str, Any],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when a chain starts running.
@@ -326,14 +327,14 @@ class CallbackManagerMixin:
 
     def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         input_str: str,
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-        inputs: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when the tool starts running.
@@ -393,8 +394,8 @@ class RunManagerMixin:
         data: Any,
         *,
         run_id: UUID,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Override to define a handler for a custom event.
@@ -470,13 +471,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_llm_start(
         self,
-        serialized: Dict[str, Any],
-        prompts: List[str],
+        serialized: dict[str, Any],
+        prompts: list[str],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when LLM starts running.
@@ -497,13 +498,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
-        messages: List[List[BaseMessage]],
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when a chat model starts running.
@@ -533,7 +534,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on new LLM token. Only available when streaming is enabled.
@@ -554,7 +555,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when LLM ends running.
@@ -573,7 +574,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when LLM errors.
@@ -590,13 +591,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_chain_start(
         self,
-        serialized: Dict[str, Any],
-        inputs: Dict[str, Any],
+        serialized: dict[str, Any],
+        inputs: dict[str, Any],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when a chain starts running.
@@ -613,11 +614,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_chain_end(
         self,
-        outputs: Dict[str, Any],
+        outputs: dict[str, Any],
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when a chain ends running.
@@ -636,7 +637,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when chain errors.
@@ -651,14 +652,14 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         input_str: str,
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-        inputs: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when the tool starts running.
@@ -680,7 +681,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when the tool ends running.
@@ -699,7 +700,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run when tool errors.
@@ -718,7 +719,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on an arbitrary text.
@@ -754,7 +755,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on agent action.
@@ -773,7 +774,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on the agent end.
@@ -788,13 +789,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_retriever_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         query: str,
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on the retriever start.
@@ -815,7 +816,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on the retriever end.
@@ -833,7 +834,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
+        tags: Optional[list[str]] = None,
         **kwargs: Any,
     ) -> None:
         """Run on retriever error.
@@ -852,8 +853,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         data: Any,
         *,
         run_id: UUID,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
         """Override to define a handler for a custom event.
@@ -880,14 +881,14 @@ class BaseCallbackManager(CallbackManagerMixin):
 
     def __init__(
         self,
-        handlers: List[BaseCallbackHandler],
-        inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
+        handlers: list[BaseCallbackHandler],
+        inheritable_handlers: Optional[list[BaseCallbackHandler]] = None,
         parent_run_id: Optional[UUID] = None,
         *,
-        tags: Optional[List[str]] = None,
-        inheritable_tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-        inheritable_metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        inheritable_tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
+        inheritable_metadata: Optional[dict[str, Any]] = None,
     ) -> None:
         """Initialize callback manager.
 
@@ -901,8 +902,8 @@ class BaseCallbackManager(CallbackManagerMixin):
                 Default is None.
             metadata (Optional[Dict[str, Any]]): The metadata. Default is None.
         """
-        self.handlers: List[BaseCallbackHandler] = handlers
-        self.inheritable_handlers: List[BaseCallbackHandler] = (
+        self.handlers: list[BaseCallbackHandler] = handlers
+        self.inheritable_handlers: list[BaseCallbackHandler] = (
             inheritable_handlers or []
         )
         self.parent_run_id: Optional[UUID] = parent_run_id
@@ -1002,7 +1003,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         self.inheritable_handlers.remove(handler)
 
     def set_handlers(
-        self, handlers: List[BaseCallbackHandler], inherit: bool = True
+        self, handlers: list[BaseCallbackHandler], inherit: bool = True
     ) -> None:
         """Set handlers as the only handlers on the callback manager.
 
@@ -1024,7 +1025,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         """
         self.set_handlers([handler], inherit=inherit)
 
-    def add_tags(self, tags: List[str], inherit: bool = True) -> None:
+    def add_tags(self, tags: list[str], inherit: bool = True) -> None:
         """Add tags to the callback manager.
 
         Args:
@@ -1038,7 +1039,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         if inherit:
             self.inheritable_tags.extend(tags)
 
-    def remove_tags(self, tags: List[str]) -> None:
+    def remove_tags(self, tags: list[str]) -> None:
         """Remove tags from the callback manager.
 
         Args:
@@ -1048,7 +1049,7 @@ class BaseCallbackManager(CallbackManagerMixin):
             self.tags.remove(tag)
             self.inheritable_tags.remove(tag)
 
-    def add_metadata(self, metadata: Dict[str, Any], inherit: bool = True) -> None:
+    def add_metadata(self, metadata: dict[str, Any], inherit: bool = True) -> None:
         """Add metadata to the callback manager.
 
         Args:
@@ -1059,7 +1060,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         if inherit:
             self.inheritable_metadata.update(metadata)
 
-    def remove_metadata(self, keys: List[str]) -> None:
+    def remove_metadata(self, keys: list[str]) -> None:
         """Remove metadata from the callback manager.
 
         Args:
@@ -1070,4 +1071,4 @@ class BaseCallbackManager(CallbackManagerMixin):
             self.inheritable_metadata.pop(key)
 
 
-Callbacks = Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]
+Callbacks = Optional[Union[list[BaseCallbackHandler], BaseCallbackManager]]

libs/core/langchain_core/callbacks/file.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Any, Dict, Optional, TextIO, cast
+from typing import Any, Optional, TextIO, cast
 
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.callbacks import BaseCallbackHandler
@@ -35,7 +35,7 @@ class FileCallbackHandler(BaseCallbackHandler):
         self.file.close()
 
     def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+        self, serialized: dict[str, Any], inputs: dict[str, Any], **kwargs: Any
     ) -> None:
         """Print out that we are entering a chain.
 
@@ -51,7 +51,7 @@ class FileCallbackHandler(BaseCallbackHandler):
             file=self.file,
         )
 
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain.
 
         Args:

libs/core/langchain_core/callbacks/manager.py

@@ -5,21 +5,15 @@ import functools
 import logging
 import uuid
 from abc import ABC, abstractmethod
+from collections.abc import AsyncGenerator, Coroutine, Generator, Sequence
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager, contextmanager
 from contextvars import copy_context
 from typing import (
     TYPE_CHECKING,
     Any,
-    AsyncGenerator,
     Callable,
-    Coroutine,
-    Dict,
-    Generator,
-    List,
     Optional,
-    Sequence,
-    Type,
     TypeVar,
     Union,
     cast,
@@ -64,12 +58,12 @@ def trace_as_chain_group(
     group_name: str,
     callback_manager: Optional[CallbackManager] = None,
     *,
-    inputs: Optional[Dict[str, Any]] = None,
+    inputs: Optional[dict[str, Any]] = None,
     project_name: Optional[str] = None,
     example_id: Optional[Union[str, UUID]] = None,
     run_id: Optional[UUID] = None,
-    tags: Optional[List[str]] = None,
-    metadata: Optional[Dict[str, Any]] = None,
+    tags: Optional[list[str]] = None,
+    metadata: Optional[dict[str, Any]] = None,
 ) -> Generator[CallbackManagerForChainGroup, None, None]:
     """Get a callback manager for a chain group in a context manager.
     Useful for grouping different calls together as a single run even if
@@ -144,12 +138,12 @@ async def atrace_as_chain_group(
     group_name: str,
     callback_manager: Optional[AsyncCallbackManager] = None,
     *,
-    inputs: Optional[Dict[str, Any]] = None,
+    inputs: Optional[dict[str, Any]] = None,
     project_name: Optional[str] = None,
     example_id: Optional[Union[str, UUID]] = None,
     run_id: Optional[UUID] = None,
-    tags: Optional[List[str]] = None,
-    metadata: Optional[Dict[str, Any]] = None,
+    tags: Optional[list[str]] = None,
+    metadata: Optional[dict[str, Any]] = None,
 ) -> AsyncGenerator[AsyncCallbackManagerForChainGroup, None]:
     """Get an async callback manager for a chain group in a context manager.
     Useful for grouping different async calls together as a single run even if
@@ -240,7 +234,7 @@ def shielded(func: Func) -> Func:
 
 
 def handle_event(
-    handlers: List[BaseCallbackHandler],
+    handlers: list[BaseCallbackHandler],
     event_name: str,
     ignore_condition_name: Optional[str],
     *args: Any,
@@ -258,10 +252,10 @@ def handle_event(
         *args: The arguments to pass to the event handler.
         **kwargs: The keyword arguments to pass to the event handler
     """
-    coros: List[Coroutine[Any, Any, Any]] = []
+    coros: list[Coroutine[Any, Any, Any]] = []
 
     try:
-        message_strings: Optional[List[str]] = None
+        message_strings: Optional[list[str]] = None
         for handler in handlers:
             try:
                 if ignore_condition_name is None or not getattr(
@@ -318,7 +312,7 @@ def handle_event(
                 _run_coros(coros)
 
 
-def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None:
+def _run_coros(coros: list[Coroutine[Any, Any, Any]]) -> None:
     if hasattr(asyncio, "Runner"):
         # Python 3.11+
         # Run the coroutines in a new event loop, taking care to
@@ -399,7 +393,7 @@ async def _ahandle_event_for_handler(
 
 
 async def ahandle_event(
-    handlers: List[BaseCallbackHandler],
+    handlers: list[BaseCallbackHandler],
     event_name: str,
     ignore_condition_name: Optional[str],
     *args: Any,
@@ -446,13 +440,13 @@ class BaseRunManager(RunManagerMixin):
         self,
         *,
         run_id: UUID,
-        handlers: List[BaseCallbackHandler],
-        inheritable_handlers: List[BaseCallbackHandler],
+        handlers: list[BaseCallbackHandler],
+        inheritable_handlers: list[BaseCallbackHandler],
         parent_run_id: Optional[UUID] = None,
-        tags: Optional[List[str]] = None,
-        inheritable_tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-        inheritable_metadata: Optional[Dict[str, Any]] = None,
+        tags: Optional[list[str]] = None,
+        inheritable_tags: Optional[list[str]] = None,
+        metadata: Optional[dict[str, Any]] = None,
+        inheritable_metadata: Optional[dict[str, Any]] = None,
     ) -> None:
         """Initialize the run manager.
 
@@ -481,7 +475,7 @@ class BaseRunManager(RunManagerMixin):
         self.inheritable_metadata = inheritable_metadata or {}
 
     @classmethod
-    def get_noop_manager(cls: Type[BRM]) -> BRM:
+    def get_noop_manager(cls: type[BRM]) -> BRM:
         """Return a manager that doesn't perform any operations.
 
         Returns:
@@ -824,7 +818,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
 class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin):
     """Callback manager for chain run."""
 
-    def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: Union[dict[str, Any], Any], **kwargs: Any) -> None:
         """Run when chain ends running.
 
         Args:
@@ -929,7 +923,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin):
 
     @shielded
     async def on_chain_end(
-        self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
+        self, outputs: Union[dict[str, Any], Any], **kwargs: Any
     ) -> None:
         """Run when a chain ends running.
 
@@ -1248,11 +1242,11 @@ class CallbackManager(BaseCallbackManager):
 
     def on_llm_start(
         self,
-        serialized: Dict[str, Any],
-        prompts: List[str],
+        serialized: dict[str, Any],
+        prompts: list[str],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
-    ) -> List[CallbackManagerForLLMRun]:
+    ) -> list[CallbackManagerForLLMRun]:
         """Run when LLM starts running.
 
         Args:
@@ -1299,11 +1293,11 @@ class CallbackManager(BaseCallbackManager):
 
     def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
-        messages: List[List[BaseMessage]],
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
-    ) -> List[CallbackManagerForLLMRun]:
+    ) -> list[CallbackManagerForLLMRun]:
         """Run when LLM starts running.
 
         Args:
@@ -1354,8 +1348,8 @@ class CallbackManager(BaseCallbackManager):
 
     def on_chain_start(
         self,
-        serialized: Optional[Dict[str, Any]],
-        inputs: Union[Dict[str, Any], Any],
+        serialized: Optional[dict[str, Any]],
+        inputs: Union[dict[str, Any], Any],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
     ) -> CallbackManagerForChainRun:
@@ -1398,11 +1392,11 @@ class CallbackManager(BaseCallbackManager):
 
     def on_tool_start(
         self,
-        serialized: Optional[Dict[str, Any]],
+        serialized: Optional[dict[str, Any]],
         input_str: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
-        inputs: Optional[Dict[str, Any]] = None,
+        inputs: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> CallbackManagerForToolRun:
         """Run when tool starts running.
@@ -1453,7 +1447,7 @@ class CallbackManager(BaseCallbackManager):
 
     def on_retriever_start(
         self,
-        serialized: Optional[Dict[str, Any]],
+        serialized: Optional[dict[str, Any]],
         query: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
@@ -1541,10 +1535,10 @@ class CallbackManager(BaseCallbackManager):
         inheritable_callbacks: Callbacks = None,
         local_callbacks: Callbacks = None,
         verbose: bool = False,
-        inheritable_tags: Optional[List[str]] = None,
-        local_tags: Optional[List[str]] = None,
-        inheritable_metadata: Optional[Dict[str, Any]] = None,
-        local_metadata: Optional[Dict[str, Any]] = None,
+        inheritable_tags: Optional[list[str]] = None,
+        local_tags: Optional[list[str]] = None,
+        inheritable_metadata: Optional[dict[str, Any]] = None,
+        local_metadata: Optional[dict[str, Any]] = None,
     ) -> CallbackManager:
         """Configure the callback manager.
 
@@ -1583,8 +1577,8 @@ class CallbackManagerForChainGroup(CallbackManager):
 
     def __init__(
         self,
-        handlers: List[BaseCallbackHandler],
-        inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
+        handlers: list[BaseCallbackHandler],
+        inheritable_handlers: Optional[list[BaseCallbackHandler]] = None,
         parent_run_id: Optional[UUID] = None,
         *,
         parent_run_manager: CallbackManagerForChainRun,
@@ -1681,7 +1675,7 @@ class CallbackManagerForChainGroup(CallbackManager):
             manager.add_handler(handler, inherit=True)
         return manager
 
-    def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: Union[dict[str, Any], Any], **kwargs: Any) -> None:
         """Run when traced chain group ends.
 
         Args:
@@ -1716,11 +1710,11 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_llm_start(
         self,
-        serialized: Dict[str, Any],
-        prompts: List[str],
+        serialized: dict[str, Any],
+        prompts: list[str],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
-    ) -> List[AsyncCallbackManagerForLLMRun]:
+    ) -> list[AsyncCallbackManagerForLLMRun]:
         """Run when LLM starts running.
 
         Args:
@@ -1779,11 +1773,11 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
-        messages: List[List[BaseMessage]],
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
-    ) -> List[AsyncCallbackManagerForLLMRun]:
+    ) -> list[AsyncCallbackManagerForLLMRun]:
         """Async run when LLM starts running.
 
         Args:
@@ -1840,8 +1834,8 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_chain_start(
         self,
-        serialized: Optional[Dict[str, Any]],
-        inputs: Union[Dict[str, Any], Any],
+        serialized: Optional[dict[str, Any]],
+        inputs: Union[dict[str, Any], Any],
         run_id: Optional[UUID] = None,
         **kwargs: Any,
     ) -> AsyncCallbackManagerForChainRun:
@@ -1886,7 +1880,7 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_tool_start(
         self,
-        serialized: Optional[Dict[str, Any]],
+        serialized: Optional[dict[str, Any]],
         input_str: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
@@ -1975,7 +1969,7 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_retriever_start(
         self,
-        serialized: Optional[Dict[str, Any]],
+        serialized: Optional[dict[str, Any]],
         query: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
@@ -2027,10 +2021,10 @@ class AsyncCallbackManager(BaseCallbackManager):
         inheritable_callbacks: Callbacks = None,
         local_callbacks: Callbacks = None,
         verbose: bool = False,
-        inheritable_tags: Optional[List[str]] = None,
-        local_tags: Optional[List[str]] = None,
-        inheritable_metadata: Optional[Dict[str, Any]] = None,
-        local_metadata: Optional[Dict[str, Any]] = None,
+        inheritable_tags: Optional[list[str]] = None,
+        local_tags: Optional[list[str]] = None,
+        inheritable_metadata: Optional[dict[str, Any]] = None,
+        local_metadata: Optional[dict[str, Any]] = None,
     ) -> AsyncCallbackManager:
         """Configure the async callback manager.
 
@@ -2069,8 +2063,8 @@ class AsyncCallbackManagerForChainGroup(AsyncCallbackManager):
 
     def __init__(
         self,
-        handlers: List[BaseCallbackHandler],
-        inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
+        handlers: list[BaseCallbackHandler],
+        inheritable_handlers: Optional[list[BaseCallbackHandler]] = None,
         parent_run_id: Optional[UUID] = None,
         *,
         parent_run_manager: AsyncCallbackManagerForChainRun,
@@ -2169,7 +2163,7 @@ class AsyncCallbackManagerForChainGroup(AsyncCallbackManager):
         return manager
 
     async def on_chain_end(
-        self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
+        self, outputs: Union[dict[str, Any], Any], **kwargs: Any
     ) -> None:
         """Run when traced chain group ends.
 
@@ -2202,14 +2196,14 @@ H = TypeVar("H", bound=BaseCallbackHandler, covariant=True)
 
 
 def _configure(
-    callback_manager_cls: Type[T],
+    callback_manager_cls: type[T],
     inheritable_callbacks: Callbacks = None,
     local_callbacks: Callbacks = None,
     verbose: bool = False,
-    inheritable_tags: Optional[List[str]] = None,
-    local_tags: Optional[List[str]] = None,
-    inheritable_metadata: Optional[Dict[str, Any]] = None,
-    local_metadata: Optional[Dict[str, Any]] = None,
+    inheritable_tags: Optional[list[str]] = None,
+    local_tags: Optional[list[str]] = None,
+    inheritable_metadata: Optional[dict[str, Any]] = None,
+    local_metadata: Optional[dict[str, Any]] = None,
 ) -> T:
     """Configure the callback manager.
 
@@ -2354,7 +2348,7 @@ def _configure(
             and handler_class is not None
         )
         if var.get() is not None or create_one:
-            var_handler = var.get() or cast(Type[BaseCallbackHandler], handler_class)()
+            var_handler = var.get() or cast(type[BaseCallbackHandler], handler_class)()
             if handler_class is None:
                 if not any(
                     handler is var_handler  # direct pointer comparison

libs/core/langchain_core/callbacks/stdout.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 from langchain_core.callbacks.base import BaseCallbackHandler
 from langchain_core.utils import print_text
@@ -23,7 +23,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         self.color = color
 
     def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+        self, serialized: dict[str, Any], inputs: dict[str, Any], **kwargs: Any
     ) -> None:
         """Print out that we are entering a chain.
 
@@ -35,7 +35,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         class_name = serialized.get("name", serialized.get("id", ["<unknown>"])[-1])
         print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m")  # noqa: T201
 
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain.
 
         Args:

libs/core/langchain_core/callbacks/streaming_stdout.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import sys
-from typing import TYPE_CHECKING, Any, Dict, List
+from typing import TYPE_CHECKING, Any
 
 from langchain_core.callbacks.base import BaseCallbackHandler
 
@@ -17,7 +17,7 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
     """Callback handler for streaming. Only works with LLMs that support streaming."""
 
     def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+        self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any
     ) -> None:
         """Run when LLM starts running.
 
@@ -29,8 +29,8 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
 
     def on_chat_model_start(
         self,
-        serialized: Dict[str, Any],
-        messages: List[List[BaseMessage]],
+        serialized: dict[str, Any],
+        messages: list[list[BaseMessage]],
         **kwargs: Any,
     ) -> None:
         """Run when LLM starts running.
@@ -68,7 +68,7 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """
 
     def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+        self, serialized: dict[str, Any], inputs: dict[str, Any], **kwargs: Any
     ) -> None:
         """Run when a chain starts running.
 
@@ -78,7 +78,7 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
             **kwargs (Any): Additional keyword arguments.
         """
 
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
         """Run when a chain ends running.
 
         Args:
@@ -95,7 +95,7 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """
 
     def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+        self, serialized: dict[str, Any], input_str: str, **kwargs: Any
     ) -> None:
         """Run when the tool starts running.
 

libs/core/langchain_core/chat_history.py

@@ -18,7 +18,8 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import List, Sequence, Union
+from collections.abc import Sequence
+from typing import Union
 
 from pydantic import BaseModel, Field
 
@@ -87,7 +88,7 @@ class BaseChatMessageHistory(ABC):
                        f.write("[]")
     """
 
-    messages: List[BaseMessage]
+    messages: list[BaseMessage]
     """A property or attribute that returns a list of messages.
 
     In general, getting the messages may involve IO to the underlying
@@ -95,7 +96,7 @@ class BaseChatMessageHistory(ABC):
     latency.
     """
 
-    async def aget_messages(self) -> List[BaseMessage]:
+    async def aget_messages(self) -> list[BaseMessage]:
         """Async version of getting messages.
 
         Can over-ride this method to provide an efficient async implementation.
@@ -204,10 +205,10 @@ class InMemoryChatMessageHistory(BaseChatMessageHistory, BaseModel):
     Stores messages in a memory list.
     """
 
-    messages: List[BaseMessage] = Field(default_factory=list)
+    messages: list[BaseMessage] = Field(default_factory=list)
     """A list of messages stored in memory."""
 
-    async def aget_messages(self) -> List[BaseMessage]:
+    async def aget_messages(self) -> list[BaseMessage]:
         """Async version of getting messages.
 
         Can over-ride this method to provide an efficient async implementation.

libs/core/langchain_core/chat_loaders.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Iterator, List
+from collections.abc import Iterator
 
 from langchain_core.chat_sessions import ChatSession
 
@@ -15,7 +15,7 @@ class BaseChatLoader(ABC):
             An iterator of chat sessions.
         """
 
-    def load(self) -> List[ChatSession]:
+    def load(self) -> list[ChatSession]:
         """Eagerly load the chat sessions into memory.
 
         Returns:

libs/core/langchain_core/chat_sessions.py

@@ -1,6 +1,7 @@
 """**Chat Sessions** are a collection of messages and function calls."""
 
-from typing import Sequence, TypedDict
+from collections.abc import Sequence
+from typing import TypedDict
 
 from langchain_core.messages import BaseMessage
 

libs/core/langchain_core/document_loaders/base.py

@@ -3,7 +3,8 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, AsyncIterator, Iterator, List, Optional
+from collections.abc import AsyncIterator, Iterator
+from typing import TYPE_CHECKING, Optional
 
 from langchain_core.documents import Document
 from langchain_core.runnables import run_in_executor
@@ -25,17 +26,17 @@ class BaseLoader(ABC):  # noqa: B024
 
     # Sub-classes should not implement this method directly. Instead, they
     # should implement the lazy load method.
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         """Load data into Document objects."""
         return list(self.lazy_load())
 
-    async def aload(self) -> List[Document]:
+    async def aload(self) -> list[Document]:
         """Load data into Document objects."""
         return [document async for document in self.alazy_load()]
 
     def load_and_split(
         self, text_splitter: Optional[TextSplitter] = None
-    ) -> List[Document]:
+    ) -> list[Document]:
         """Load Documents and split into chunks. Chunks are returned as Documents.
 
         Do not override this method. It should be considered to be deprecated!
@@ -108,7 +109,7 @@ class BaseBlobParser(ABC):
             Generator of documents
         """
 
-    def parse(self, blob: Blob) -> List[Document]:
+    def parse(self, blob: Blob) -> list[Document]:
         """Eagerly parse the blob into a document or documents.
 
         This is a convenience method for interactive development environment.

libs/core/langchain_core/document_loaders/blob_loaders.py

@@ -8,7 +8,7 @@ In addition, content loading code should provide a lazy loading interface by def
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Iterable
+from collections.abc import Iterable
 
 # Re-export Blob and PathLike for backwards compatibility
 from langchain_core.documents.base import Blob as Blob

libs/core/langchain_core/document_loaders/langsmith.py

@@ -1,7 +1,8 @@
 import datetime
 import json
 import uuid
-from typing import Any, Callable, Iterator, Optional, Sequence, Union
+from collections.abc import Iterator, Sequence
+from typing import Any, Callable, Optional, Union
 
 from langsmith import Client as LangSmithClient
 

libs/core/langchain_core/documents/base.py

@@ -2,9 +2,10 @@ from __future__ import annotations
 
 import contextlib
 import mimetypes
+from collections.abc import Generator
 from io import BufferedReader, BytesIO
 from pathlib import PurePath
-from typing import Any, Dict, Generator, List, Literal, Optional, Union, cast
+from typing import Any, Literal, Optional, Union, cast
 
 from pydantic import ConfigDict, Field, field_validator, model_validator
 
@@ -138,7 +139,7 @@ class Blob(BaseMedia):
 
     @model_validator(mode="before")
     @classmethod
-    def check_blob_is_valid(cls, values: Dict[str, Any]) -> Any:
+    def check_blob_is_valid(cls, values: dict[str, Any]) -> Any:
         """Verify that either data or path is provided."""
         if "data" not in values and "path" not in values:
             raise ValueError("Either data or path must be provided")
@@ -285,7 +286,7 @@ class Document(BaseMedia):
         return True
 
     @classmethod
-    def get_lc_namespace(cls) -> List[str]:
+    def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the langchain object."""
         return ["langchain", "schema", "document"]
 

libs/core/langchain_core/documents/compressor.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Optional, Sequence
+from collections.abc import Sequence
+from typing import Optional
 
 from pydantic import BaseModel
 

libs/core/langchain_core/documents/transformers.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Sequence
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
 
 from langchain_core.runnables.config import run_in_executor
 

libs/core/langchain_core/embeddings/embeddings.py

@@ -1,7 +1,6 @@
 """**Embeddings** interface."""
 
 from abc import ABC, abstractmethod
-from typing import List
 
 from langchain_core.runnables.config import run_in_executor
 
@@ -35,7 +34,7 @@ class Embeddings(ABC):
     """
 
     @abstractmethod
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Embed search docs.
 
         Args:
@@ -46,7 +45,7 @@ class Embeddings(ABC):
         """
 
     @abstractmethod
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Embed query text.
 
         Args:
@@ -56,7 +55,7 @@ class Embeddings(ABC):
             Embedding.
         """
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
         """Asynchronous Embed search docs.
 
         Args:
@@ -67,7 +66,7 @@ class Embeddings(ABC):
         """
         return await run_in_executor(None, self.embed_documents, texts)
 
-    async def aembed_query(self, text: str) -> List[float]:
+    async def aembed_query(self, text: str) -> list[float]:
         """Asynchronous Embed query text.
 
         Args:

libs/core/langchain_core/embeddings/fake.py

@@ -2,7 +2,6 @@
 
 # Please do not add additional fake embedding model implementations here.
 import hashlib
-from typing import List
 
 from pydantic import BaseModel
 
@@ -51,15 +50,15 @@ class FakeEmbeddings(Embeddings, BaseModel):
     size: int
     """The size of the embedding vector."""
 
-    def _get_embedding(self) -> List[float]:
+    def _get_embedding(self) -> list[float]:
         import numpy as np  # type: ignore[import-not-found, import-untyped]
 
         return list(np.random.normal(size=self.size))
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         return [self._get_embedding() for _ in texts]
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         return self._get_embedding()
 
 
@@ -106,7 +105,7 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
     size: int
     """The size of the embedding vector."""
 
-    def _get_embedding(self, seed: int) -> List[float]:
+    def _get_embedding(self, seed: int) -> list[float]:
         import numpy as np  # type: ignore[import-not-found, import-untyped]
 
         # set the seed for the random generator
@@ -117,8 +116,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
         """Get a seed for the random generator, using the hash of the text."""
         return int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 10**8
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         return self._get_embedding(seed=self._get_seed(text))

libs/core/langchain_core/example_selectors/base.py

@@ -1,7 +1,7 @@
 """Interface for selecting examples to include in prompts."""
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List
+from typing import Any
 
 from langchain_core.runnables import run_in_executor
 
@@ -10,14 +10,14 @@ class BaseExampleSelector(ABC):
     """Interface for selecting examples to include in prompts."""
 
     @abstractmethod
-    def add_example(self, example: Dict[str, str]) -> Any:
+    def add_example(self, example: dict[str, str]) -> Any:
         """Add new example to store.
 
         Args:
             example: A dictionary with keys as input variables
                 and values as their values."""
 
-    async def aadd_example(self, example: Dict[str, str]) -> Any:
+    async def aadd_example(self, example: dict[str, str]) -> Any:
         """Async add new example to store.
 
         Args:
@@ -27,14 +27,14 @@ class BaseExampleSelector(ABC):
         return await run_in_executor(None, self.add_example, example)
 
     @abstractmethod
-    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    def select_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Select which examples to use based on the inputs.
 
         Args:
             input_variables: A dictionary with keys as input variables
                 and values as their values."""
 
-    async def aselect_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    async def aselect_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Async select which examples to use based on the inputs.
 
         Args:

libs/core/langchain_core/example_selectors/length_based.py

@@ -1,7 +1,7 @@
 """Select examples based on length."""
 
 import re
-from typing import Callable, Dict, List
+from typing import Callable
 
 from pydantic import BaseModel, Field, model_validator
 from typing_extensions import Self
@@ -17,7 +17,7 @@ def _get_length_based(text: str) -> int:
 class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
     """Select examples based on length."""
 
-    examples: List[dict]
+    examples: list[dict]
     """A list of the examples that the prompt template expects."""
 
     example_prompt: PromptTemplate
@@ -29,10 +29,10 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
     max_length: int = 2048
     """Max length for the prompt, beyond which examples are cut."""
 
-    example_text_lengths: List[int] = Field(default_factory=list)  # :meta private:
+    example_text_lengths: list[int] = Field(default_factory=list)  # :meta private:
     """Length of each example."""
 
-    def add_example(self, example: Dict[str, str]) -> None:
+    def add_example(self, example: dict[str, str]) -> None:
         """Add new example to list.
 
         Args:
@@ -43,7 +43,7 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
         string_example = self.example_prompt.format(**example)
         self.example_text_lengths.append(self.get_text_length(string_example))
 
-    async def aadd_example(self, example: Dict[str, str]) -> None:
+    async def aadd_example(self, example: dict[str, str]) -> None:
         """Async add new example to list.
 
         Args:
@@ -62,7 +62,7 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
         self.example_text_lengths = [self.get_text_length(eg) for eg in string_examples]
         return self
 
-    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    def select_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Select which examples to use based on the input lengths.
 
         Args:
@@ -86,7 +86,7 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
             i += 1
         return examples
 
-    async def aselect_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    async def aselect_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Async select which examples to use based on the input lengths.
 
         Args:

libs/core/langchain_core/example_selectors/semantic_similarity.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from abc import ABC
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
+from typing import TYPE_CHECKING, Any, Optional
 
 from pydantic import BaseModel, ConfigDict
 
@@ -15,7 +15,7 @@ if TYPE_CHECKING:
     from langchain_core.embeddings import Embeddings
 
 
-def sorted_values(values: Dict[str, str]) -> List[Any]:
+def sorted_values(values: dict[str, str]) -> list[Any]:
     """Return a list of values in dict sorted by key.
 
     Args:
@@ -35,12 +35,12 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
     """VectorStore that contains information about examples."""
     k: int = 4
     """Number of examples to select."""
-    example_keys: Optional[List[str]] = None
+    example_keys: Optional[list[str]] = None
     """Optional keys to filter examples to."""
-    input_keys: Optional[List[str]] = None
+    input_keys: Optional[list[str]] = None
     """Optional keys to filter input to. If provided, the search is based on
     the input variables instead of all variables."""
-    vectorstore_kwargs: Optional[Dict[str, Any]] = None
+    vectorstore_kwargs: Optional[dict[str, Any]] = None
     """Extra arguments passed to similarity_search function of the vectorstore."""
 
     model_config = ConfigDict(
@@ -50,14 +50,14 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
 
     @staticmethod
     def _example_to_text(
-        example: Dict[str, str], input_keys: Optional[List[str]]
+        example: dict[str, str], input_keys: Optional[list[str]]
     ) -> str:
         if input_keys:
             return " ".join(sorted_values({key: example[key] for key in input_keys}))
         else:
             return " ".join(sorted_values(example))
 
-    def _documents_to_examples(self, documents: List[Document]) -> List[dict]:
+    def _documents_to_examples(self, documents: list[Document]) -> list[dict]:
         # Get the examples from the metadata.
         # This assumes that examples are stored in metadata.
         examples = [dict(e.metadata) for e in documents]
@@ -66,7 +66,7 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
             examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
         return examples
 
-    def add_example(self, example: Dict[str, str]) -> str:
+    def add_example(self, example: dict[str, str]) -> str:
         """Add a new example to vectorstore.
 
         Args:
@@ -81,7 +81,7 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
         )
         return ids[0]
 
-    async def aadd_example(self, example: Dict[str, str]) -> str:
+    async def aadd_example(self, example: dict[str, str]) -> str:
         """Async add new example to vectorstore.
 
         Args:
@@ -100,7 +100,7 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
 class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
     """Select examples based on semantic similarity."""
 
-    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    def select_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Select examples based on semantic similarity.
 
         Args:
@@ -118,7 +118,7 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
         )
         return self._documents_to_examples(example_docs)
 
-    async def aselect_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    async def aselect_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Asynchronously select examples based on semantic similarity.
 
         Args:
@@ -139,13 +139,13 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
     @classmethod
     def from_examples(
         cls,
-        examples: List[dict],
+        examples: list[dict],
         embeddings: Embeddings,
-        vectorstore_cls: Type[VectorStore],
+        vectorstore_cls: type[VectorStore],
         k: int = 4,
-        input_keys: Optional[List[str]] = None,
+        input_keys: Optional[list[str]] = None,
         *,
-        example_keys: Optional[List[str]] = None,
+        example_keys: Optional[list[str]] = None,
         vectorstore_kwargs: Optional[dict] = None,
         **vectorstore_cls_kwargs: Any,
     ) -> SemanticSimilarityExampleSelector:
@@ -183,13 +183,13 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
     @classmethod
     async def afrom_examples(
         cls,
-        examples: List[dict],
+        examples: list[dict],
         embeddings: Embeddings,
-        vectorstore_cls: Type[VectorStore],
+        vectorstore_cls: type[VectorStore],
         k: int = 4,
-        input_keys: Optional[List[str]] = None,
+        input_keys: Optional[list[str]] = None,
         *,
-        example_keys: Optional[List[str]] = None,
+        example_keys: Optional[list[str]] = None,
         vectorstore_kwargs: Optional[dict] = None,
         **vectorstore_cls_kwargs: Any,
     ) -> SemanticSimilarityExampleSelector:
@@ -235,7 +235,7 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
     fetch_k: int = 20
     """Number of examples to fetch to rerank."""
 
-    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    def select_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Select examples based on Max Marginal Relevance.
 
         Args:
@@ -251,7 +251,7 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
         )
         return self._documents_to_examples(example_docs)
 
-    async def aselect_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+    async def aselect_examples(self, input_variables: dict[str, str]) -> list[dict]:
         """Asynchronously select examples based on Max Marginal Relevance.
 
         Args:
@@ -270,13 +270,13 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
     @classmethod
     def from_examples(
         cls,
-        examples: List[dict],
+        examples: list[dict],
         embeddings: Embeddings,
-        vectorstore_cls: Type[VectorStore],
+        vectorstore_cls: type[VectorStore],
         k: int = 4,
-        input_keys: Optional[List[str]] = None,
+        input_keys: Optional[list[str]] = None,
         fetch_k: int = 20,
-        example_keys: Optional[List[str]] = None,
+        example_keys: Optional[list[str]] = None,
         vectorstore_kwargs: Optional[dict] = None,
         **vectorstore_cls_kwargs: Any,
     ) -> MaxMarginalRelevanceExampleSelector:
@@ -317,14 +317,14 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
     @classmethod
     async def afrom_examples(
         cls,
-        examples: List[dict],
+        examples: list[dict],
         embeddings: Embeddings,
-        vectorstore_cls: Type[VectorStore],
+        vectorstore_cls: type[VectorStore],
         *,
         k: int = 4,
-        input_keys: Optional[List[str]] = None,
+        input_keys: Optional[list[str]] = None,
         fetch_k: int = 20,
-        example_keys: Optional[List[str]] = None,
+        example_keys: Optional[list[str]] = None,
         vectorstore_kwargs: Optional[dict] = None,
         **vectorstore_cls_kwargs: Any,
     ) -> MaxMarginalRelevanceExampleSelector:

libs/core/langchain_core/exceptions.py

@@ -3,7 +3,7 @@
 from typing import Any, Optional
 
 
-class LangChainException(Exception):
+class LangChainException(Exception):  # noqa: N818
     """General LangChain exception."""
 
 
@@ -11,7 +11,7 @@ class TracerException(LangChainException):
     """Base class for exceptions in tracers module."""
 
 
-class OutputParserException(ValueError, LangChainException):
+class OutputParserException(ValueError, LangChainException):  # noqa: N818
     """Exception that output parsers should raise to signify a parsing error.
 
     This exists to differentiate parsing errors from other code or execution errors

libs/core/langchain_core/graph_vectorstores/__init__.py

@@ -1,15 +0,0 @@
-from langchain_core.graph_vectorstores.base import (
-    GraphVectorStore,
-    GraphVectorStoreRetriever,
-    Node,
-)
-from langchain_core.graph_vectorstores.links import (
-    Link,
-)
-
-__all__ = [
-    "GraphVectorStore",
-    "GraphVectorStoreRetriever",
-    "Node",
-    "Link",
-]

libs/core/langchain_core/graph_vectorstores/base.py

@@ -1,712 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from typing import (
-    Any,
-    AsyncIterable,
-    ClassVar,
-    Collection,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-)
-
-from pydantic import Field
-
-from langchain_core._api import beta
-from langchain_core.callbacks import (
-    AsyncCallbackManagerForRetrieverRun,
-    CallbackManagerForRetrieverRun,
-)
-from langchain_core.documents import Document
-from langchain_core.graph_vectorstores.links import METADATA_LINKS_KEY, Link
-from langchain_core.load import Serializable
-from langchain_core.runnables import run_in_executor
-from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
-
-
-def _has_next(iterator: Iterator) -> bool:
-    """Checks if the iterator has more elements.
-    Warning: consumes an element from the iterator"""
-    sentinel = object()
-    return next(iterator, sentinel) is not sentinel
-
-
-@beta()
-class Node(Serializable):
-    """Node in the GraphVectorStore.
-
-    Edges exist from nodes with an outgoing link to nodes with a matching incoming link.
-
-    For instance two nodes `a` and `b` connected over a hyperlink ``https://some-url``
-    would look like:
-
-    .. code-block:: python
-
-        [
-            Node(
-                id="a",
-                text="some text a",
-                links= [
-                    Link(kind="hyperlink", tag="https://some-url", direction="incoming")
-                ],
-            ),
-            Node(
-                id="b",
-                text="some text b",
-                links= [
-                    Link(kind="hyperlink", tag="https://some-url", direction="outgoing")
-                ],
-            )
-        ]
-    """
-
-    id: Optional[str] = None
-    """Unique ID for the node. Will be generated by the GraphVectorStore if not set."""
-    text: str
-    """Text contained by the node."""
-    metadata: dict = Field(default_factory=dict)
-    """Metadata for the node."""
-    links: List[Link] = Field(default_factory=list)
-    """Links associated with the node."""
-
-
-def _texts_to_nodes(
-    texts: Iterable[str],
-    metadatas: Optional[Iterable[dict]],
-    ids: Optional[Iterable[str]],
-) -> Iterator[Node]:
-    metadatas_it = iter(metadatas) if metadatas else None
-    ids_it = iter(ids) if ids else None
-    for text in texts:
-        try:
-            _metadata = next(metadatas_it).copy() if metadatas_it else {}
-        except StopIteration as e:
-            raise ValueError("texts iterable longer than metadatas") from e
-        try:
-            _id = next(ids_it) if ids_it else None
-        except StopIteration as e:
-            raise ValueError("texts iterable longer than ids") from e
-
-        links = _metadata.pop(METADATA_LINKS_KEY, [])
-        if not isinstance(links, list):
-            links = list(links)
-        yield Node(
-            id=_id,
-            metadata=_metadata,
-            text=text,
-            links=links,
-        )
-    if ids_it and _has_next(ids_it):
-        raise ValueError("ids iterable longer than texts")
-    if metadatas_it and _has_next(metadatas_it):
-        raise ValueError("metadatas iterable longer than texts")
-
-
-def _documents_to_nodes(documents: Iterable[Document]) -> Iterator[Node]:
-    for doc in documents:
-        metadata = doc.metadata.copy()
-        links = metadata.pop(METADATA_LINKS_KEY, [])
-        if not isinstance(links, list):
-            links = list(links)
-        yield Node(
-            id=doc.id,
-            metadata=metadata,
-            text=doc.page_content,
-            links=links,
-        )
-
-
-@beta()
-def nodes_to_documents(nodes: Iterable[Node]) -> Iterator[Document]:
-    """Convert nodes to documents.
-
-    Args:
-        nodes: The nodes to convert to documents.
-    Returns:
-        The documents generated from the nodes.
-    """
-    for node in nodes:
-        metadata = node.metadata.copy()
-        metadata[METADATA_LINKS_KEY] = [
-            # Convert the core `Link` (from the node) back to the local `Link`.
-            Link(kind=link.kind, direction=link.direction, tag=link.tag)
-            for link in node.links
-        ]
-
-        yield Document(
-            id=node.id,
-            page_content=node.text,
-            metadata=metadata,
-        )
-
-
-@beta(message="Added in version 0.2.14 of langchain_core. API subject to change.")
-class GraphVectorStore(VectorStore):
-    """A hybrid vector-and-graph graph store.
-
-    Document chunks support vector-similarity search as well as edges linking
-    chunks based on structural and semantic properties.
-
-    .. versionadded:: 0.2.14
-    """
-
-    @abstractmethod
-    def add_nodes(
-        self,
-        nodes: Iterable[Node],
-        **kwargs: Any,
-    ) -> Iterable[str]:
-        """Add nodes to the graph store.
-
-        Args:
-            nodes: the nodes to add.
-        """
-
-    async def aadd_nodes(
-        self,
-        nodes: Iterable[Node],
-        **kwargs: Any,
-    ) -> AsyncIterable[str]:
-        """Add nodes to the graph store.
-
-        Args:
-            nodes: the nodes to add.
-        """
-        iterator = iter(await run_in_executor(None, self.add_nodes, nodes, **kwargs))
-        done = object()
-        while True:
-            doc = await run_in_executor(None, next, iterator, done)
-            if doc is done:
-                break
-            yield doc  # type: ignore[misc]
-
-    def add_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[Iterable[dict]] = None,
-        *,
-        ids: Optional[Iterable[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        The Links present in the metadata field `links` will be extracted to create
-        the `Node` links.
-
-        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
-        function call would look like:
-
-        .. code-block:: python
-
-            store.add_texts(
-                ids=["a", "b"],
-                texts=["some text a", "some text b"],
-                metadatas=[
-                    {
-                        "links": [
-                            Link.incoming(kind="hyperlink", tag="https://some-url")
-                        ]
-                    },
-                    {
-                        "links": [
-                            Link.outgoing(kind="hyperlink", tag="https://some-url")
-                        ]
-                    },
-                ],
-            )
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            metadatas: Optional list of metadatas associated with the texts.
-                The metadata key `links` shall be an iterable of
-                :py:class:`~langchain_core.graph_vectorstores.links.Link`.
-            **kwargs: vectorstore specific parameters.
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        nodes = _texts_to_nodes(texts, metadatas, ids)
-        return list(self.add_nodes(nodes, **kwargs))
-
-    async def aadd_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[Iterable[dict]] = None,
-        *,
-        ids: Optional[Iterable[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        The Links present in the metadata field `links` will be extracted to create
-        the `Node` links.
-
-        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
-        function call would look like:
-
-        .. code-block:: python
-
-            await store.aadd_texts(
-                ids=["a", "b"],
-                texts=["some text a", "some text b"],
-                metadatas=[
-                    {
-                        "links": [
-                            Link.incoming(kind="hyperlink", tag="https://some-url")
-                        ]
-                    },
-                    {
-                        "links": [
-                            Link.outgoing(kind="hyperlink", tag="https://some-url")
-                        ]
-                    },
-                ],
-            )
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            metadatas: Optional list of metadatas associated with the texts.
-                The metadata key `links` shall be an iterable of
-                :py:class:`~langchain_core.graph_vectorstores.links.Link`.
-            **kwargs: vectorstore specific parameters.
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        nodes = _texts_to_nodes(texts, metadatas, ids)
-        return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
-
-    def add_documents(
-        self,
-        documents: Iterable[Document],
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        The Links present in the document metadata field `links` will be extracted to
-        create the `Node` links.
-
-        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
-        function call would look like:
-
-        .. code-block:: python
-
-            store.add_documents(
-                [
-                    Document(
-                        id="a",
-                        page_content="some text a",
-                        metadata={
-                            "links": [
-                                Link.incoming(kind="hyperlink", tag="http://some-url")
-                            ]
-                        }
-                    ),
-                    Document(
-                        id="b",
-                        page_content="some text b",
-                        metadata={
-                            "links": [
-                                Link.outgoing(kind="hyperlink", tag="http://some-url")
-                            ]
-                        }
-                    ),
-                ]
-
-            )
-
-        Args:
-            documents: Documents to add to the vectorstore.
-                The document's metadata key `links` shall be an iterable of
-                :py:class:`~langchain_core.graph_vectorstores.links.Link`.
-
-        Returns:
-            List of IDs of the added texts.
-        """
-        nodes = _documents_to_nodes(documents)
-        return list(self.add_nodes(nodes, **kwargs))
-
-    async def aadd_documents(
-        self,
-        documents: Iterable[Document],
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        The Links present in the document metadata field `links` will be extracted to
-        create the `Node` links.
-
-        Eg if nodes `a` and `b` are connected over a hyperlink `https://some-url`, the
-        function call would look like:
-
-        .. code-block:: python
-
-            store.add_documents(
-                [
-                    Document(
-                        id="a",
-                        page_content="some text a",
-                        metadata={
-                            "links": [
-                                Link.incoming(kind="hyperlink", tag="http://some-url")
-                            ]
-                        }
-                    ),
-                    Document(
-                        id="b",
-                        page_content="some text b",
-                        metadata={
-                            "links": [
-                                Link.outgoing(kind="hyperlink", tag="http://some-url")
-                            ]
-                        }
-                    ),
-                ]
-
-            )
-
-        Args:
-            documents: Documents to add to the vectorstore.
-                The document's metadata key `links` shall be an iterable of
-                :py:class:`~langchain_core.graph_vectorstores.links.Link`.
-
-        Returns:
-            List of IDs of the added texts.
-        """
-        nodes = _documents_to_nodes(documents)
-        return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
-
-    @abstractmethod
-    def traversal_search(
-        self,
-        query: str,
-        *,
-        k: int = 4,
-        depth: int = 1,
-        **kwargs: Any,
-    ) -> Iterable[Document]:
-        """Retrieve documents from traversing this graph store.
-
-        First, `k` nodes are retrieved using a search for each `query` string.
-        Then, additional nodes are discovered up to the given `depth` from those
-        starting nodes.
-
-        Args:
-            query: The query string.
-            k: The number of Documents to return from the initial search.
-                Defaults to 4. Applies to each of the query strings.
-            depth: The maximum depth of edges to traverse. Defaults to 1.
-        Returns:
-            Retrieved documents.
-        """
-
-    async def atraversal_search(
-        self,
-        query: str,
-        *,
-        k: int = 4,
-        depth: int = 1,
-        **kwargs: Any,
-    ) -> AsyncIterable[Document]:
-        """Retrieve documents from traversing this graph store.
-
-        First, `k` nodes are retrieved using a search for each `query` string.
-        Then, additional nodes are discovered up to the given `depth` from those
-        starting nodes.
-
-        Args:
-            query: The query string.
-            k: The number of Documents to return from the initial search.
-                Defaults to 4. Applies to each of the query strings.
-            depth: The maximum depth of edges to traverse. Defaults to 1.
-        Returns:
-            Retrieved documents.
-        """
-        iterator = iter(
-            await run_in_executor(
-                None, self.traversal_search, query, k=k, depth=depth, **kwargs
-            )
-        )
-        done = object()
-        while True:
-            doc = await run_in_executor(None, next, iterator, done)
-            if doc is done:
-                break
-            yield doc  # type: ignore[misc]
-
-    @abstractmethod
-    def mmr_traversal_search(
-        self,
-        query: str,
-        *,
-        k: int = 4,
-        depth: int = 2,
-        fetch_k: int = 100,
-        adjacent_k: int = 10,
-        lambda_mult: float = 0.5,
-        score_threshold: float = float("-inf"),
-        **kwargs: Any,
-    ) -> Iterable[Document]:
-        """Retrieve documents from this graph store using MMR-traversal.
-
-        This strategy first retrieves the top `fetch_k` results by similarity to
-        the question. It then selects the top `k` results based on
-        maximum-marginal relevance using the given `lambda_mult`.
-
-        At each step, it considers the (remaining) documents from `fetch_k` as
-        well as any documents connected by edges to a selected document
-        retrieved based on similarity (a "root").
-
-        Args:
-            query: The query string to search for.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch via similarity.
-                Defaults to 100.
-            adjacent_k: Number of adjacent Documents to fetch.
-                Defaults to 10.
-            depth: Maximum depth of a node (number of edges) from a node
-                retrieved via similarity. Defaults to 2.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                of diversity among the results with 0 corresponding to maximum
-                diversity and 1 to minimum diversity. Defaults to 0.5.
-            score_threshold: Only documents with a score greater than or equal
-                this threshold will be chosen. Defaults to negative infinity.
-        """
-
-    async def ammr_traversal_search(
-        self,
-        query: str,
-        *,
-        k: int = 4,
-        depth: int = 2,
-        fetch_k: int = 100,
-        adjacent_k: int = 10,
-        lambda_mult: float = 0.5,
-        score_threshold: float = float("-inf"),
-        **kwargs: Any,
-    ) -> AsyncIterable[Document]:
-        """Retrieve documents from this graph store using MMR-traversal.
-
-        This strategy first retrieves the top `fetch_k` results by similarity to
-        the question. It then selects the top `k` results based on
-        maximum-marginal relevance using the given `lambda_mult`.
-
-        At each step, it considers the (remaining) documents from `fetch_k` as
-        well as any documents connected by edges to a selected document
-        retrieved based on similarity (a "root").
-
-        Args:
-            query: The query string to search for.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch via similarity.
-                Defaults to 100.
-            adjacent_k: Number of adjacent Documents to fetch.
-                Defaults to 10.
-            depth: Maximum depth of a node (number of edges) from a node
-                retrieved via similarity. Defaults to 2.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                of diversity among the results with 0 corresponding to maximum
-                diversity and 1 to minimum diversity. Defaults to 0.5.
-            score_threshold: Only documents with a score greater than or equal
-                this threshold will be chosen. Defaults to negative infinity.
-        """
-        iterator = iter(
-            await run_in_executor(
-                None,
-                self.mmr_traversal_search,
-                query,
-                k=k,
-                fetch_k=fetch_k,
-                adjacent_k=adjacent_k,
-                depth=depth,
-                lambda_mult=lambda_mult,
-                score_threshold=score_threshold,
-                **kwargs,
-            )
-        )
-        done = object()
-        while True:
-            doc = await run_in_executor(None, next, iterator, done)
-            if doc is done:
-                break
-            yield doc  # type: ignore[misc]
-
-    def similarity_search(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        return list(self.traversal_search(query, k=k, depth=0))
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        return list(
-            self.mmr_traversal_search(
-                query, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, depth=0
-            )
-        )
-
-    async def asimilarity_search(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        return [doc async for doc in self.atraversal_search(query, k=k, depth=0)]
-
-    def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
-        if search_type == "similarity":
-            return self.similarity_search(query, **kwargs)
-        elif search_type == "similarity_score_threshold":
-            docs_and_similarities = self.similarity_search_with_relevance_scores(
-                query, **kwargs
-            )
-            return [doc for doc, _ in docs_and_similarities]
-        elif search_type == "mmr":
-            return self.max_marginal_relevance_search(query, **kwargs)
-        elif search_type == "traversal":
-            return list(self.traversal_search(query, **kwargs))
-        elif search_type == "mmr_traversal":
-            return list(self.mmr_traversal_search(query, **kwargs))
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity', 'similarity_score_threshold', "
-                "'mmr' or 'traversal'."
-            )
-
-    async def asearch(
-        self, query: str, search_type: str, **kwargs: Any
-    ) -> List[Document]:
-        if search_type == "similarity":
-            return await self.asimilarity_search(query, **kwargs)
-        elif search_type == "similarity_score_threshold":
-            docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
-                query, **kwargs
-            )
-            return [doc for doc, _ in docs_and_similarities]
-        elif search_type == "mmr":
-            return await self.amax_marginal_relevance_search(query, **kwargs)
-        elif search_type == "traversal":
-            return [doc async for doc in self.atraversal_search(query, **kwargs)]
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity', 'similarity_score_threshold', "
-                "'mmr' or 'traversal'."
-            )
-
-    def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
-        """Return GraphVectorStoreRetriever initialized from this GraphVectorStore.
-
-        Args:
-            **kwargs: Keyword arguments to pass to the search function.
-                Can include:
-
-                - search_type (Optional[str]): Defines the type of search that
-                  the Retriever should perform.
-                  Can be ``traversal`` (default), ``similarity``, ``mmr``, or
-                  ``similarity_score_threshold``.
-                - search_kwargs (Optional[Dict]): Keyword arguments to pass to the
-                  search function. Can include things like:
-
-                  - k(int): Amount of documents to return (Default: 4).
-                  - depth(int): The maximum depth of edges to traverse (Default: 1).
-                  - score_threshold(float): Minimum relevance threshold
-                    for similarity_score_threshold.
-                  - fetch_k(int): Amount of documents to pass to MMR algorithm
-                    (Default: 20).
-                  - lambda_mult(float): Diversity of results returned by MMR;
-                    1 for minimum diversity and 0 for maximum. (Default: 0.5).
-        Returns:
-            Retriever for this GraphVectorStore.
-
-        Examples:
-
-        .. code-block:: python
-
-            # Retrieve documents traversing edges
-            docsearch.as_retriever(
-                search_type="traversal",
-                search_kwargs={'k': 6, 'depth': 3}
-            )
-
-            # Retrieve more documents with higher diversity
-            # Useful if your dataset has many similar documents
-            docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 6, 'lambda_mult': 0.25}
-            )
-
-            # Fetch more documents for the MMR algorithm to consider
-            # But only return the top 5
-            docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 5, 'fetch_k': 50}
-            )
-
-            # Only retrieve documents that have a relevance score
-            # Above a certain threshold
-            docsearch.as_retriever(
-                search_type="similarity_score_threshold",
-                search_kwargs={'score_threshold': 0.8}
-            )
-
-            # Only get the single most similar document from the dataset
-            docsearch.as_retriever(search_kwargs={'k': 1})
-
-        """
-        return GraphVectorStoreRetriever(vectorstore=self, **kwargs)
-
-
-class GraphVectorStoreRetriever(VectorStoreRetriever):
-    """Retriever class for GraphVectorStore."""
-
-    vectorstore: GraphVectorStore
-    """GraphVectorStore to use for retrieval."""
-    search_type: str = "traversal"
-    """Type of search to perform. Defaults to "traversal"."""
-    allowed_search_types: ClassVar[Collection[str]] = (
-        "similarity",
-        "similarity_score_threshold",
-        "mmr",
-        "traversal",
-        "mmr_traversal",
-    )
-
-    def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        if self.search_type == "traversal":
-            return list(self.vectorstore.traversal_search(query, **self.search_kwargs))
-        elif self.search_type == "mmr_traversal":
-            return list(
-                self.vectorstore.mmr_traversal_search(query, **self.search_kwargs)
-            )
-        else:
-            return super()._get_relevant_documents(query, run_manager=run_manager)
-
-    async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        if self.search_type == "traversal":
-            return [
-                doc
-                async for doc in self.vectorstore.atraversal_search(
-                    query, **self.search_kwargs
-                )
-            ]
-        elif self.search_type == "mmr_traversal":
-            return [
-                doc
-                async for doc in self.vectorstore.ammr_traversal_search(
-                    query, **self.search_kwargs
-                )
-            ]
-        else:
-            return await super()._aget_relevant_documents(
-                query, run_manager=run_manager
-            )

libs/core/langchain_core/graph_vectorstores/links.py

@@ -1,101 +0,0 @@
-from dataclasses import dataclass
-from typing import Iterable, List, Literal, Union
-
-from langchain_core._api import beta
-from langchain_core.documents import Document
-
-
-@beta()
-@dataclass(frozen=True)
-class Link:
-    """A link to/from a tag of a given tag.
-
-    Edges exist from nodes with an outgoing link to nodes with a matching incoming link.
-    """
-
-    kind: str
-    """The kind of link. Allows different extractors to use the same tag name without
-    creating collisions between extractors. For example “keyword” vs “url”."""
-    direction: Literal["in", "out", "bidir"]
-    """The direction of the link."""
-    tag: str
-    """The tag of the link."""
-
-    @staticmethod
-    def incoming(kind: str, tag: str) -> "Link":
-        """Create an incoming link."""
-        return Link(kind=kind, direction="in", tag=tag)
-
-    @staticmethod
-    def outgoing(kind: str, tag: str) -> "Link":
-        """Create an outgoing link."""
-        return Link(kind=kind, direction="out", tag=tag)
-
-    @staticmethod
-    def bidir(kind: str, tag: str) -> "Link":
-        """Create a bidirectional link."""
-        return Link(kind=kind, direction="bidir", tag=tag)
-
-
-METADATA_LINKS_KEY = "links"
-
-
-@beta()
-def get_links(doc: Document) -> List[Link]:
-    """Get the links from a document.
-
-    Args:
-        doc: The document to get the link tags from.
-    Returns:
-        The set of link tags from the document.
-    """
-
-    links = doc.metadata.setdefault(METADATA_LINKS_KEY, [])
-    if not isinstance(links, list):
-        # Convert to a list and remember that.
-        links = list(links)
-        doc.metadata[METADATA_LINKS_KEY] = links
-    return links
-
-
-@beta()
-def add_links(doc: Document, *links: Union[Link, Iterable[Link]]) -> None:
-    """Add links to the given metadata.
-
-    Args:
-        doc: The document to add the links to.
-        *links: The links to add to the document.
-    """
-    links_in_metadata = get_links(doc)
-    for link in links:
-        if isinstance(link, Iterable):
-            links_in_metadata.extend(link)
-        else:
-            links_in_metadata.append(link)
-
-
-@beta()
-def copy_with_links(doc: Document, *links: Union[Link, Iterable[Link]]) -> Document:
-    """Return a document with the given links added.
-
-    Args:
-        doc: The document to add the links to.
-        *links: The links to add to the document.
-
-    Returns:
-        A document with a shallow-copy of the metadata with the links added.
-    """
-    new_links = set(get_links(doc))
-    for link in links:
-        if isinstance(link, Iterable):
-            new_links.update(link)
-        else:
-            new_links.add(link)
-
-    return Document(
-        page_content=doc.page_content,
-        metadata={
-            **doc.metadata,
-            METADATA_LINKS_KEY: list(new_links),
-        },
-    )

libs/core/langchain_core/indexing/api.py

@@ -5,20 +5,13 @@ from __future__ import annotations
 import hashlib
 import json
 import uuid
+from collections.abc import AsyncIterable, AsyncIterator, Iterable, Iterator, Sequence
 from itertools import islice
 from typing import (
     Any,
-    AsyncIterable,
-    AsyncIterator,
     Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
     Literal,
     Optional,
-    Sequence,
-    Set,
     TypedDict,
     TypeVar,
     Union,
@@ -71,7 +64,7 @@ class _HashedDocument(Document):
 
     @model_validator(mode="before")
     @classmethod
-    def calculate_hashes(cls, values: Dict[str, Any]) -> Any:
+    def calculate_hashes(cls, values: dict[str, Any]) -> Any:
         """Root validator to calculate content and metadata hash."""
         content = values.get("page_content", "")
         metadata = values.get("metadata", {})
@@ -125,7 +118,7 @@ class _HashedDocument(Document):
         )
 
 
-def _batch(size: int, iterable: Iterable[T]) -> Iterator[List[T]]:
+def _batch(size: int, iterable: Iterable[T]) -> Iterator[list[T]]:
     """Utility batching function."""
     it = iter(iterable)
     while True:
@@ -135,9 +128,9 @@ def _batch(size: int, iterable: Iterable[T]) -> Iterator[List[T]]:
         yield chunk
 
 
-async def _abatch(size: int, iterable: AsyncIterable[T]) -> AsyncIterator[List[T]]:
+async def _abatch(size: int, iterable: AsyncIterable[T]) -> AsyncIterator[list[T]]:
     """Utility batching function."""
-    batch: List[T] = []
+    batch: list[T] = []
     async for element in iterable:
         if len(batch) < size:
             batch.append(element)
@@ -171,7 +164,7 @@ def _deduplicate_in_order(
     hashed_documents: Iterable[_HashedDocument],
 ) -> Iterator[_HashedDocument]:
     """Deduplicate a list of hashed documents while preserving order."""
-    seen: Set[str] = set()
+    seen: set[str] = set()
 
     for hashed_doc in hashed_documents:
         if hashed_doc.hash_ not in seen:
@@ -349,7 +342,7 @@ def index(
         uids = []
         docs_to_index = []
         uids_to_refresh = []
-        seen_docs: Set[str] = set()
+        seen_docs: set[str] = set()
         for hashed_doc, doc_exists in zip(hashed_docs, exists_batch):
             if doc_exists:
                 if force_update:
@@ -589,7 +582,7 @@ async def aindex(
         uids: list[str] = []
         docs_to_index: list[Document] = []
         uids_to_refresh = []
-        seen_docs: Set[str] = set()
+        seen_docs: set[str] = set()
         for hashed_doc, doc_exists in zip(hashed_docs, exists_batch):
             if doc_exists:
                 if force_update: