Merge branch 'master' into erick/skip-release-check-cli

.github/workflows/_release.yml

@@ -14,7 +14,7 @@ env:
 
 jobs:
   build:
-    if: github.ref == 'refs/heads/master'
+    # if: github.ref == 'refs/heads/master'
     runs-on: ubuntu-latest
 
     outputs:
@@ -70,58 +70,59 @@ jobs:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
-  pre-release-checks:
-    needs:
-      - build
-      - test-pypi-publish
-    runs-on: ubuntu-latest
-    steps:
-      # We explicitly *don't* set up caching here. This ensures our tests are
-      # maximally sensitive to catching breakage.
-      #
-      # For example, here's a way that caching can cause a falsely-passing test:
-      # - Make the langchain package manifest no longer list a dependency package
-      #   as a requirement. This means it won't be installed by `pip install`,
-      #   and attempting to use it would cause a crash.
-      # - That dependency used to be required, so it may have been cached.
-      #   When restoring the venv packages from cache, that dependency gets included.
-      # - Tests pass, because the dependency is present even though it wasn't specified.
-      # - The package is published, and it breaks on the missing dependency when
-      #   used in the real world.
-      - uses: actions/setup-python@v4
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
+  # pre-release-checks:
+  #   needs:
+  #     - build
+  #     - test-pypi-publish
+  #   runs-on: ubuntu-latest
+  #   steps:
+  #     # We explicitly *don't* set up caching here. This ensures our tests are
+  #     # maximally sensitive to catching breakage.
+  #     #
+  #     # For example, here's a way that caching can cause a falsely-passing test:
+  #     # - Make the langchain package manifest no longer list a dependency package
+  #     #   as a requirement. This means it won't be installed by `pip install`,
+  #     #   and attempting to use it would cause a crash.
+  #     # - That dependency used to be required, so it may have been cached.
+  #     #   When restoring the venv packages from cache, that dependency gets included.
+  #     # - Tests pass, because the dependency is present even though it wasn't specified.
+  #     # - The package is published, and it breaks on the missing dependency when
+  #     #   used in the real world.
+  #     - uses: actions/setup-python@v4
+  #       with:
+  #         python-version: ${{ env.PYTHON_VERSION }}
 
-      - name: Test published package
-        shell: bash
-        env:
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          VERSION: ${{ needs.build.outputs.version }}
-        # Here we use:
-        # - The default regular PyPI index as the *primary* index, meaning 
-        #   that it takes priority (https://pypi.org/simple)
-        # - The test PyPI index as an extra index, so that any dependencies that
-        #   are not found on test PyPI can be resolved and installed anyway.
-        #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
-        #   package because VERSION will not have been uploaded to regular PyPI yet.
-        #
-        # TODO: add more in-depth pre-publish tests after testing that importing works
-        run: |
-          pip install \
-            --extra-index-url https://test.pypi.org/simple/ \
-            "$PKG_NAME==$VERSION"
+  #     - name: Test published package
+  #       shell: bash
+  #       env:
+  #         PKG_NAME: ${{ needs.build.outputs.pkg-name }}
+  #         VERSION: ${{ needs.build.outputs.version }}
+  #       # Here we specify:
+  #       # - The test PyPI index as the *primary* index, meaning that it takes priority.
+  #       # - The regular PyPI index as an extra index, so that any dependencies that
+  #       #   are not found on test PyPI can be resolved and installed anyway.
+  #       #
+  #       # Without the former, we might install the wrong langchain release.
+  #       # Without the latter, we might not be able to install langchain's dependencies.
+  #       #
+  #       # TODO: add more in-depth pre-publish tests after testing that importing works
+  #       run: |
+  #         pip install \
+  #           --index-url https://test.pypi.org/simple/ \
+  #           --extra-index-url https://pypi.org/simple/ \
+  #           "$PKG_NAME==$VERSION"
 
-          # Replace all dashes in the package name with underscores,
-          # since that's how Python imports packages with dashes in the name.
-          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
+  #         # Replace all dashes in the package name with underscores,
+  #         # since that's how Python imports packages with dashes in the name.
+  #         IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
 
-          python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+  #         python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
   publish:
     needs:
       - build
       - test-pypi-publish
-      - pre-release-checks
+      # - pre-release-checks
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:
@@ -162,7 +163,7 @@ jobs:
     needs:
       - build
       - test-pypi-publish
-      - pre-release-checks
+      # - pre-release-checks
       - publish
     runs-on: ubuntu-latest
     permissions:

.github/workflows/_test_release.yml

@@ -14,7 +14,7 @@ env:
 
 jobs:
   build:
-    if: github.ref == 'refs/heads/master'
+    # if: github.ref == 'refs/heads/master'
     runs-on: ubuntu-latest
 
     outputs:

cookbook/README.md

@@ -8,7 +8,6 @@ Notebook | Description
 [Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
 [Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.
 [Semi_structured_multi_modal_RA...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using various tools and methods such as unstructured for parsing, multi-vector retriever for storing, lcel for implementing chains, and open source language models like llama2, llava, and gpt4all.
-[analyze_document.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/analyze_document.ipynb) | Analyze a single long document.
 [autogpt/autogpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/autogpt.ipynb) | Implement autogpt, a language model, with langchain primitives such as llms, prompttemplates, vectorstores, embeddings, and tools.
 [autogpt/marathon_times.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/marathon_times.ipynb) | Implement autogpt for finding winning marathon times.
 [baby_agi.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi.ipynb) | Implement babyagi, an ai agent that can generate and execute tasks based on a given objective, with the flexibility to swap out specific vectorstores/model providers.
@@ -45,7 +44,6 @@ Notebook | Description
 [plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent.
 [press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
 [program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
-[qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
 [retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
 [sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
 [self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.

cookbook/analyze_document.ipynb

@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f69d4a4c-137d-47e9-bea1-786afce9c1c0",
-   "metadata": {},
-   "source": [
-    "# Analyze a single long document\n",
-    "\n",
-    "The AnalyzeDocumentChain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2a0707ce-6d2d-471b-bc33-64da32a7b3f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"../docs/docs/modules/state_of_the_union.txt\") as f:\n",
-    "    state_of_the_union = f.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ca14d161-2d5b-4a6c-a296-77d8ce4b28cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import AnalyzeDocumentChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9f97406c-85a9-45fb-99ce-9138c0ba3731",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.question_answering import load_qa_chain\n",
-    "\n",
-    "qa_chain = load_qa_chain(llm, chain_type=\"map_reduce\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0871a753-f5bb-4b4f-a394-f87f2691f659",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e6f86428-3c2c-46a0-a57c-e22826fdbf91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The President said, \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qa_document_chain.run(\n",
-    "    input_document=state_of_the_union,\n",
-    "    question=\"what did the president say about justice breyer?\",\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/api_reference/_static/css/custom.css

@@ -15,11 +15,3 @@ pre {
 #my-component-root *, #headlessui-portal-root * {
   z-index: 10000;
 }
-
-table.longtable code {
-  white-space: normal;
-}
-
-table.longtable td {
-  max-width: 600px;
-}

docs/docs/expression_language/index.mdx

@@ -30,4 +30,4 @@ As your chains get more and more complex, it becomes increasingly important to u
 With LCEL, **all** steps are automatically logged to [LangSmith](/docs/langsmith/) for maximum observability and debuggability.
 
 **Seamless LangServe deployment integration**
-Any chain created with LCEL can be easily deployed using [LangServe](/docs/langserve).
+Any chain created with LCEL can be easily deployed using LangServe.

docs/docs/get_started/installation.mdx

@@ -29,7 +29,7 @@ If you want to install from source, you can do so by cloning the repo and be sur
 pip install -e .
 ```
 
-## LangChain experimental
+## Langchain experimental
 The `langchain-experimental` package holds experimental LangChain code, intended for research and experimental uses.
 Install with:
 
@@ -37,6 +37,14 @@ Install with:
 pip install langchain-experimental
 ```
 
+## LangChain CLI
+The LangChain CLI is useful for working with LangChain templates and other LangServe projects.
+Install with:
+
+```bash
+pip install langchain-cli
+```
+
 ## LangServe
 LangServe helps developers deploy LangChain runnables and chains as a REST API.
 LangServe is automatically installed by LangChain CLI.
@@ -47,14 +55,6 @@ pip install "langserve[all]"
 ```
 for both client and server dependencies. Or `pip install "langserve[client]"` for client code, and `pip install "langserve[server]"` for server code.
 
-## LangChain CLI
-The LangChain CLI is useful for working with LangChain templates and other LangServe projects.
-Install with:
-
-```bash
-pip install langchain-cli
-```
-
 ## LangSmith SDK
 The LangSmith SDK is automatically installed by LangChain.
 If not using LangChain, install with:

docs/docs/get_started/quickstart.mdx

@@ -4,7 +4,7 @@ In this quickstart we'll show you how to:
 - Get setup with LangChain, LangSmith and LangServe
 - Use the most basic and common components of LangChain: prompt templates, models, and output parsers
 - Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining
-- Build a simple application with LangChain
+- Build simple application with LangChain
 - Trace your application with LangSmith
 - Serve your application with LangServe
 

docs/docs/integrations/llms/huggingface_hub.ipynb

@@ -101,8 +101,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
+    "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {
@@ -397,46 +397,10 @@
     "print(llm_chain.run(question))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e3871376-ed0e-49a8-8d9b-7e60dbbd2b35",
-   "metadata": {},
-   "source": [
-    "### `Yi` series models, by `01.ai`\n",
-    "\n",
-    ">The `Yi` series models are large language models trained from scratch by developers at [01.ai](https://01.ai/). The first public release contains two bilingual(English/Chinese) base models with the parameter sizes of 6B(`Yi-6B`) and 34B(`Yi-34B`). Both of them are trained with 4K sequence length and can be extended to 32K during inference time. The `Yi-6B-200K` and `Yi-34B-200K` are base model with 200K context length.\n",
-    "\n",
-    "Here we test the [Yi-34B](https://huggingface.co/01-ai/Yi-34B) model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1c9d3125-3f50-48b8-93b6-b50847207afa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"01-ai/Yi-34B\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b661069-8229-4850-9f13-c4ca28c0c96b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd6f3edc-9f97-47a6-ab2c-116756babbe6",
+   "id": "1dd67c1e-1efc-4def-bde4-2e5265725303",
    "metadata": {},
    "outputs": [],
    "source": []

docs/docs/integrations/vectorstores/pgvector_async.ipynb

@@ -1,363 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PGVector with async connections\n",
-    "\n",
-    ">[PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for `Postgres`\n",
-    "\n",
-    "It supports:\n",
-    "- exact and approximate nearest neighbor search\n",
-    "- L2 distance, inner product, and cosine distance\n",
-    "\n",
-    "This notebook shows how to use the Postgres vector database (`PGVector`) with async connections."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See the [installation instruction](https://github.com/pgvector/pgvector)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Pip install necessary package\n",
-    "!pip install pgvector\n",
-    "!pip install openai\n",
-    "!pip install asyncpg\n",
-    "!pip install greenlet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Loading Environment Variables\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "load_dotenv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores.pgvector_async import PGVectorAsync\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.docstore.document import Document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# PGVectorAsync need the database url to connect to the database.\n",
-    "\n",
-    "DATABASE_URL = \"postgresql+asyncpg://postgres:postgres@localhost:5432/postgres\"\n",
-    "\n",
-    "# Alternatively, you can pass a async engine to PGVectorAsync\n",
-    "# engine = create_async_engine(url=DATABASE_URL, echo=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up your database\n",
-    "\n",
-    "You only need to run this once, preferably in a migration script."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = PGVectorAsync(\n",
-    "    embeddings=embeddings,\n",
-    "    db_url=DATABASE_URL,\n",
-    ")\n",
-    "\n",
-    "# Alternatively, you can pass a async engine to PGVectorAsync\n",
-    "# vectorstore = PGVectorAsync(\n",
-    "#     embeddings=embeddings,\n",
-    "#     engine=engine,\n",
-    "# )\n",
-    "\n",
-    "await vectorstore.create_schema()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Similarity Search with Euclidean Distance (Default)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "COLLECTION_NAME = \"state_of_the_union_test\"\n",
-    "\n",
-    "vectorstore = await PGVectorAsync.afrom_documents(\n",
-    "    embedding=embeddings,\n",
-    "    documents=docs,\n",
-    "    db_url=DATABASE_URL,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs_with_score = await vectorstore.asimilarity_search_with_score(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for doc, score in docs_with_score:\n",
-    "    print(\"-\" * 80)\n",
-    "    print(\"Score: \", score)\n",
-    "    print(doc.page_content)\n",
-    "    print(\"-\" * 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Maximal Marginal Relevance Search (MMR)\n",
-    "\n",
-    "Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs_with_score = await vectorstore.amax_marginal_relevance_search_with_score(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for doc, score in docs_with_score:\n",
-    "    print(\"-\" * 80)\n",
-    "    print(\"Score: \", score)\n",
-    "    print(doc.page_content)\n",
-    "    print(\"-\" * 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Working with vectorstore\n",
-    "\n",
-    "Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.\n",
-    "In order to do that, we can initialize it directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = PGVectorAsync(\n",
-    "    collection_name=COLLECTION_NAME,\n",
-    "    embeddings=embeddings,\n",
-    "    db_url=DATABASE_URL,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Add documents\n",
-    "\n",
-    "We can add documents to the existing vectorstore."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "await vectorstore.aadd_documents(documents=[Document(page_content=\"foo\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs_with_score = await vectorstore.asimilarity_search_with_score(\"foo\", k=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "docs_with_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overriding a vectorstore\n",
-    "\n",
-    "If you have an existing collection, you override it by doing `from_documents` and setting `pre_delete_collection` = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = [Document(page_content=\"foo\"), Document(page_content=\"bar\")]\n",
-    "vectorstore = await PGVectorAsync.afrom_documents(\n",
-    "    collection_name=COLLECTION_NAME,\n",
-    "    embedding=embeddings,\n",
-    "    db_url=DATABASE_URL,\n",
-    "    documents=docs,\n",
-    "    pre_delete_collection=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs_with_score = await vectorstore.asimilarity_search_with_score(\"foo\", k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs_with_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using a VectorStore as a Retriever"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "await retriever.aget_relevant_documents(query=\"foo\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/use_cases/question_answering/analyze_document.mdx

@@ -0,0 +1,44 @@
+# Analyze a single long document
+
+The AnalyzeDocumentChain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.
+
+```python
+with open("../../state_of_the_union.txt") as f:
+    state_of_the_union = f.read()
+```
+
+```python
+from langchain.llms import OpenAI
+from langchain.chains import AnalyzeDocumentChain
+
+
+llm = OpenAI(temperature=0)
+```
+
+
+```python
+from langchain.chains.question_answering import load_qa_chain
+```
+
+
+```python
+qa_chain = load_qa_chain(llm, chain_type="map_reduce")
+```
+
+
+```python
+qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)
+```
+
+
+```python
+qa_document_chain.run(input_document=state_of_the_union, question="what did the president say about justice breyer?")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' The president thanked Justice Breyer for his service.'
+```
+
+</CodeOutputBlock>

docs/docs/use_cases/question_answering/chat_vector_db.mdx

@@ -0,0 +1,434 @@
+---
+sidebar_position: 2
+---
+
+# Remembering chat history
+The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.
+
+It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.
+
+To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.
+
+```python
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+```
+
+Load in documents. You can replace this with a loader for whatever type of data you want
+
+```python
+from langchain.document_loaders import TextLoader
+loader = TextLoader("../../state_of_the_union.txt")
+documents = loader.load()
+```
+
+If you had multiple loaders that you wanted to combine, you do something like:
+
+```python
+# loaders = [....]
+# docs = []
+# for loader in loaders:
+#     docs.extend(loader.load())
+```
+
+We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them.
+
+```python
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+documents = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+vectorstore = Chroma.from_documents(documents, embeddings)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    Using embedded DuckDB without persistence: data will be transient
+```
+
+</CodeOutputBlock>
+
+We can now create a memory object, which is necessary to track the inputs/outputs and hold a conversation.
+
+```python
+from langchain.memory import ConversationBufferMemory
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+```
+
+We now initialize the `ConversationalRetrievalChain`
+
+```python
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)
+```
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query})
+```
+
+```python
+result["answer"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+```python
+query = "Did he mention who she succeeded"
+result = qa({"question": query})
+```
+
+```python
+result['answer']
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'
+```
+
+</CodeOutputBlock>
+
+## Pass in chat history
+
+In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.
+
+```python
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())
+```
+
+Here's an example of asking a question with no chat history
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+```python
+result["answer"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+Here's an example of asking a question with some chat history
+
+```python
+chat_history = [(query, result["answer"])]
+query = "Did he mention who she succeeded"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+```python
+result['answer']
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'
+```
+
+</CodeOutputBlock>
+
+## Using a different model for condensing the question
+
+This chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is necessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.
+
+```python
+from langchain.chat_models import ChatOpenAI
+```
+
+```python
+qa = ConversationalRetrievalChain.from_llm(
+    ChatOpenAI(temperature=0, model="gpt-4"),
+    vectorstore.as_retriever(),
+    condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),
+)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+```python
+chat_history = [(query, result["answer"])]
+query = "Did he mention who she succeeded"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+## Using a custom prompt for condensing the question
+
+By default, ConversationalRetrievalQA uses CONDENSE_QUESTION_PROMPT to condense a question. Here is the implementation of this in the docs
+
+```python
+from langchain.prompts.prompt import PromptTemplate
+
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+```
+
+But instead of this any custom template can be used to further augment information in the question or instruct the LLM to do something. Here is an example
+
+```python
+from langchain.prompts.prompt import PromptTemplate
+```
+
+```python
+custom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. At the end of standalone question add this 'Answer the question in German language.' If you do not know the answer reply with 'I am sorry'.
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""
+```
+
+```python
+CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
+```
+
+```python
+model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)
+embeddings = OpenAIEmbeddings()
+vectordb = Chroma(embedding_function=embeddings, persist_directory=directory)
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+qa = ConversationalRetrievalChain.from_llm(
+    model,
+    vectordb.as_retriever(),
+    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
+    memory=memory
+)
+```
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query})
+```
+
+```python
+query = "Did he mention who she succeeded"
+result = qa({"question": query})
+```
+
+## Return Source Documents
+
+You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.
+
+```python
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+```python
+result['source_documents'][0]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})
+```
+
+</CodeOutputBlock>
+
+## ConversationalRetrievalChain with `search_distance`
+
+If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.
+
+```python
+vectordbkwargs = {"search_distance": 0.9}
+```
+
+```python
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})
+```
+
+## ConversationalRetrievalChain with `map_reduce`
+
+We can also use different types of combine document chains with the ConversationalRetrievalChain chain.
+
+```python
+from langchain.chains import LLMChain
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
+```
+
+```python
+llm = OpenAI(temperature=0)
+question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
+doc_chain = load_qa_chain(llm, chain_type="map_reduce")
+
+chain = ConversationalRetrievalChain(
+    retriever=vectorstore.as_retriever(),
+    question_generator=question_generator,
+    combine_docs_chain=doc_chain,
+)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = chain({"question": query, "chat_history": chat_history})
+```
+
+```python
+result['answer']
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+## ConversationalRetrievalChain with Question Answering with sources
+
+You can also use this chain with the question answering with sources chain.
+
+```python
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+```
+
+```python
+llm = OpenAI(temperature=0)
+question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
+doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")
+
+chain = ConversationalRetrievalChain(
+    retriever=vectorstore.as_retriever(),
+    question_generator=question_generator,
+    combine_docs_chain=doc_chain,
+)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = chain({"question": query, "chat_history": chat_history})
+```
+
+```python
+result['answer']
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \nSOURCES: ../../state_of_the_union.txt"
+```
+
+</CodeOutputBlock>
+
+## ConversationalRetrievalChain with streaming to `stdout`
+
+Output from the chain will be streamed to `stdout` token by token in this example.
+
+```python
+from langchain.chains.llm import LLMChain
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
+from langchain.chains.question_answering import load_qa_chain
+
+# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
+# and a separate, non-streaming llm for question generation
+llm = OpenAI(temperature=0)
+streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
+
+question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
+doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)
+
+qa = ConversationalRetrievalChain(
+    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+<CodeOutputBlock lang="python">
+
+```
+     The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.
+```
+
+</CodeOutputBlock>
+
+```python
+chat_history = [(query, result["answer"])]
+query = "Did he mention who she succeeded"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+<CodeOutputBlock lang="python">
+
+```
+     Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.
+```
+
+</CodeOutputBlock>
+
+## get_chat_history Function
+
+You can also specify a `get_chat_history` function, which can be used to format the chat_history string.
+
+```python
+def get_chat_history(inputs) -> str:
+    res = []
+    for human, ai in inputs:
+        res.append(f"Human:{human}\nAI:{ai}")
+    return "\n".join(res)
+qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)
+```
+
+```python
+chat_history = []
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"question": query, "chat_history": chat_history})
+```
+
+```python
+result['answer']
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>

docs/docs/use_cases/question_answering/code_understanding.ipynb

@@ -367,7 +367,7 @@
    "source": [
     "from langchain.callbacks.manager import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.chains import ConversationalRetrievalChain, LLMChain\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.llms import LlamaCpp\n",
     "from langchain.memory import ConversationSummaryMemory\n",
     "from langchain.prompts import PromptTemplate"

docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb

@@ -5,7 +5,7 @@
    "id": "839f3c76",
    "metadata": {},
    "source": [
-    "# RAG with Agents\n",
+    "# Agent with retrieval tool\n",
     "\n",
     "This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation.\n",
     "\n",

docs/docs/use_cases/question_answering/in_memory_question_answering.mdx

@@ -0,0 +1,448 @@
+# RAG over in-memory documents
+
+Here we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our [Document chains](/docs/modules/chains/document/).
+
+## Prepare Data
+First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).
+
+
+```python
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.docstore.document import Document
+from langchain.prompts import PromptTemplate
+from langchain.indexes.vectorstore import VectorstoreIndexCreator
+```
+
+
+```python
+with open("../../state_of_the_union.txt") as f:
+    state_of_the_union = f.read()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_text(state_of_the_union)
+
+embeddings = OpenAIEmbeddings()
+```
+
+
+```python
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    Running Chroma using direct local API.
+    Using DuckDB in-memory for database. Data will be transient.
+```
+
+</CodeOutputBlock>
+
+
+```python
+query = "What did the president say about Justice Breyer"
+docs = docsearch.get_relevant_documents(query)
+```
+
+
+```python
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+```
+
+## Quickstart
+If you just want to get started as quickly as possible, this is the recommended way to do it:
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+query = "What did the president say about Justice Breyer"
+chain.run(input_documents=docs, question=query)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'
+```
+
+</CodeOutputBlock>
+
+If you want more control and understanding over what is happening, please see the information below.
+
+## The `stuff` Chain
+
+This sections shows results of using the `stuff` Chain to do question answering.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+```
+
+
+```python
+query = "What did the president say about Justice Breyer"
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'output_text': ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'}
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+Question: {question}
+Answer in Italian:"""
+PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}
+```
+
+</CodeOutputBlock>
+
+## The `map_reduce` Chain
+
+This sections shows results of using the `map_reduce` Chain to do question answering.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce")
+```
+
+
+```python
+query = "What did the president say about Justice Breyer"
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
+```
+
+</CodeOutputBlock>
+
+**Intermediate Steps**
+
+We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_map_steps` variable.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True)
+```
+
+
+```python
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': [' "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service."',
+      ' A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.',
+      ' None',
+      ' None'],
+     'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.
+Return any relevant text translated into italian.
+{context}
+Question: {question}
+Relevant text, if any, in Italian:"""
+QUESTION_PROMPT = PromptTemplate(
+    template=question_prompt_template, input_variables=["context", "question"]
+)
+
+combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.
+If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+
+QUESTION: {question}
+=========
+{summaries}
+=========
+Answer in Italian:"""
+COMBINE_PROMPT = PromptTemplate(
+    template=combine_prompt_template, input_variables=["summaries", "question"]
+)
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.",
+      '\nNessun testo pertinente.',
+      ' Non ha detto nulla riguardo a Justice Breyer.',
+      " Non c'è testo pertinente."],
+     'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}
+```
+
+</CodeOutputBlock>
+
+**Batch Size**
+
+When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:
+
+```python
+llm = OpenAI(batch_size=5, temperature=0)
+```
+
+## The `refine` Chain
+
+This sections shows results of using the `refine` Chain to do question answering.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine")
+```
+
+
+```python
+query = "What did the president say about Justice Breyer"
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.'}
+```
+
+</CodeOutputBlock>
+
+**Intermediate Steps**
+
+We can also return the intermediate steps for `refine` chains, should we want to inspect them. This is done with the `return_refine_steps` variable.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True)
+```
+
+
+```python
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': ['\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',
+      '\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',
+      '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.',
+      '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'],
+     'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+refine_prompt_template = (
+    "The original question is as follows: {question}\n"
+    "We have provided an existing answer: {existing_answer}\n"
+    "We have the opportunity to refine the existing answer"
+    "(only if needed) with some more context below.\n"
+    "------------\n"
+    "{context_str}\n"
+    "------------\n"
+    "Given the new context, refine the original answer to better "
+    "answer the question. "
+    "If the context isn't useful, return the original answer. Reply in Italian."
+)
+refine_prompt = PromptTemplate(
+    input_variables=["question", "existing_answer", "context_str"],
+    template=refine_prompt_template,
+)
+
+
+initial_qa_template = (
+    "Context information is below. \n"
+    "---------------------\n"
+    "{context_str}"
+    "\n---------------------\n"
+    "Given the context information and not prior knowledge, "
+    "answer the question: {question}\nYour answer should be in Italian.\n"
+)
+initial_qa_prompt = PromptTemplate(
+    input_variables=["context_str", "question"], template=initial_qa_template
+)
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True,
+                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': ['\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.',
+      "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.",
+      "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.",
+      "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"],
+     'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"}
+```
+
+</CodeOutputBlock>
+
+## The `map-rerank` Chain
+
+This sections shows results of using the `map-rerank` Chain to do question answering with sources.
+
+
+```python
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True)
+```
+
+
+```python
+query = "What did the president say about Justice Breyer"
+results = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+
+```python
+results["output_text"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'
+```
+
+</CodeOutputBlock>
+
+
+```python
+results["intermediate_steps"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    [{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
+      'score': '100'},
+     {'answer': ' This document does not answer the question', 'score': '0'},
+     {'answer': ' This document does not answer the question', 'score': '0'},
+     {'answer': ' This document does not answer the question', 'score': '0'}]
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+from langchain.output_parsers import RegexParser
+
+output_parser = RegexParser(
+    regex=r"(.*?)\nScore: (.*)",
+    output_keys=["answer", "score"],
+)
+
+prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
+
+Question: [question here]
+Helpful Answer In Italian: [answer here]
+Score: [score between 0 and 100]
+
+Begin!
+
+Context:
+---------
+{context}
+---------
+Question: {question}
+Helpful Answer In Italian:"""
+PROMPT = PromptTemplate(
+    template=prompt_template,
+    input_variables=["context", "question"],
+    output_parser=output_parser,
+)
+
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True, prompt=PROMPT)
+query = "What did the president say about Justice Breyer"
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.',
+       'score': '100'},
+      {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
+       'score': '100'},
+      {'answer': ' Non so.', 'score': '0'},
+      {'answer': ' Non so.', 'score': '0'}],
+     'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}
+```
+
+</CodeOutputBlock>
+
+## Document QA with sources
+
+We can also perform document QA and return the sources that were used to answer the question. To do this we'll just need to make sure each document has a "source" key in the metadata, and we'll use the `load_qa_with_sources` helper to construct our chain:
+
+```python
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
+query = "What did the president say about Justice Breyer"
+docs = docsearch.similarity_search(query)
+```
+
+```python
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+
+chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
+query = "What did the president say about Justice Breyer"
+chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
+```
+
+</CodeOutputBlock>
+

docs/docs/use_cases/question_answering/multi_retrieval_qa_router.mdx

@@ -0,0 +1,128 @@
+# Dynamically select from multiple retrievers
+
+This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.
+
+```python
+from langchain.chains.router import MultiRetrievalQAChain
+from langchain.llms import OpenAI
+```
+
+
+```python
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.document_loaders import TextLoader
+from langchain.vectorstores import FAISS
+
+sou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()
+sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()
+
+pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()
+pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()
+
+personal_texts = [
+    "I love apple pie",
+    "My favorite color is fuchsia",
+    "My dream is to become a professional dancer",
+    "I broke my arm when I was 12",
+    "My parents are from Peru",
+]
+personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()
+```
+
+
+```python
+retriever_infos = [
+    {
+        "name": "state of the union",
+        "description": "Good for answering questions about the 2023 State of the Union address",
+        "retriever": sou_retriever
+    },
+    {
+        "name": "pg essay",
+        "description": "Good for answering questions about Paul Graham's essay on his career",
+        "retriever": pg_retriever
+    },
+    {
+        "name": "personal",
+        "description": "Good for answering questions about me",
+        "retriever": personal_retriever
+    }
+]
+```
+
+
+```python
+chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)
+```
+
+
+```python
+print(chain.run("What did the president say about the economy?"))
+```
+
+<CodeOutputBlock lang="python">
+
+```
+
+
+    > Entering new MultiRetrievalQAChain chain...
+    state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union address?'}
+    > Finished chain.
+     The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.
+```
+
+</CodeOutputBlock>
+
+
+```python
+print(chain.run("What is something Paul Graham regrets about his work?"))
+```
+
+<CodeOutputBlock lang="python">
+
+```
+
+
+    > Entering new MultiRetrievalQAChain chain...
+    pg essay: {'query': 'What is something Paul Graham regrets about his work?'}
+    > Finished chain.
+     Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.
+```
+
+</CodeOutputBlock>
+
+
+```python
+print(chain.run("What is my background?"))
+```
+
+<CodeOutputBlock lang="python">
+
+```
+
+
+    > Entering new MultiRetrievalQAChain chain...
+    personal: {'query': 'What is my background?'}
+    > Finished chain.
+     Your background is Peruvian.
+```
+
+</CodeOutputBlock>
+
+
+```python
+print(chain.run("What year was the Internet created in?"))
+```
+
+<CodeOutputBlock lang="python">
+
+```
+
+
+    > Entering new MultiRetrievalQAChain chain...
+    None: {'query': 'What year was the Internet created in?'}
+    > Finished chain.
+    The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.
+```
+
+</CodeOutputBlock>

docs/docs/use_cases/question_answering/multiple_retrieval.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66398b75",
+   "metadata": {},
+   "source": [
+    "# Retrieving from multiple sources\n",
+    "\n",
+    "Often times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different databases altogether!\n",
+    "\n",
+    "A key part is is doing as much of the retrieval in parallel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parallelism out of the box.\n",
+    "\n",
+    "Let's take a look where we do retrieval over a SQL database and a vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c5bab6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43a6210f",
+   "metadata": {},
+   "source": [
+    "## Set up SQL query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ab3bf8ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_sql_query_chain\n",
+    "from langchain.utilities import SQLDatabase\n",
+    "\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
+    "query_chain = create_sql_query_chain(ChatOpenAI(temperature=0), db)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8585120",
+   "metadata": {},
+   "source": [
+    "## Set up vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b916b0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.schema.document import Document\n",
+    "\n",
+    "index_creator = VectorstoreIndexCreator()\n",
+    "index = index_creator.from_documents([Document(page_content=\"Foo\")])\n",
+    "retriever = index.vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b91816",
+   "metadata": {},
+   "source": [
+    "## Combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4423211c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "system_message = \"\"\"Use the information from the below two sources to answer any questions.\n",
+    "\n",
+    "Source 1: a SQL database about employee data\n",
+    "<source1>\n",
+    "{source1}\n",
+    "</source1>\n",
+    "\n",
+    "Source 2: a text database of random information\n",
+    "<source2>\n",
+    "{source2}\n",
+    "</source2>\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [(\"system\", system_message), (\"human\", \"{question}\")]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7ff87e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_chain = (\n",
+    "    {\n",
+    "        \"source1\": {\"question\": lambda x: x[\"question\"]} | query_chain | db.run,\n",
+    "        \"source2\": (lambda x: x[\"question\"]) | retriever,\n",
+    "        \"question\": lambda x: x[\"question\"],\n",
+    "    }\n",
+    "    | prompt\n",
+    "    | ChatOpenAI()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d6706410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='There are 8 employees.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = full_chain.invoke({\"question\": \"How many Employees are there\"})\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/use_cases/question_answering/vector_db_qa.mdx

@@ -0,0 +1,230 @@
+---
+sidebar_position: 1
+---
+# Using a Retriever
+
+This example showcases question answering over an index.
+
+```python
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+```
+
+
+```python
+loader = TextLoader("../../state_of_the_union.txt")
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+docsearch = Chroma.from_documents(texts, embeddings)
+
+qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())
+```
+
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+qa.run(query)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+## Chain Type
+You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see [this notebook](/docs/modules/chains/additional/question_answering).
+
+There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `map_reduce`.
+
+
+```python
+qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())
+```
+
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+qa.run(query)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering)) and then pass that directly to the RetrievalQA chain with the `combine_documents_chain` parameter. For example:
+
+
+```python
+from langchain.chains.question_answering import load_qa_chain
+qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())
+```
+
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+qa.run(query)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+## Custom Prompts
+You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](/docs/modules/chains/additional/question_answering)
+
+
+```python
+from langchain.prompts import PromptTemplate
+prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+Question: {question}
+Answer in Italian:"""
+PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+```
+
+
+```python
+chain_type_kwargs = {"prompt": PROMPT}
+qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
+```
+
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+qa.run(query)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " Il presidente ha detto che Ketanji Brown Jackson è una delle menti legali più importanti del paese, che continuerà l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."
+```
+
+</CodeOutputBlock>
+
+## Vectorstore Retriever Options
+You can adjust how documents are retrieved from your vectorstore depending on the specific task.
+
+There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the `search_type` parameter:
+
+```python
+docsearch.as_retriever(search_type="mmr")
+```
+
+You can also modify the search by passing specific search arguments through the retriever to the search function, using the `search_kwargs` keyword argument.
+
+- `k` defines how many documents are returned; defaults to 4.
+- `score_threshold` allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.
+- `fetch_k` determines the amount of documents to pass to the MMR algorithm; defaults to 20.
+- `lambda_mult` controls the diversity of results returned by the MMR algorithm, with 1 being minimum diversity and 0 being maximum. Defaults to 0.5.
+- `filter` allows you to define a filter on what documents should be retrieved, based on the documents' metadata. This has no effect if the Vectorstore doesn't store any metadata.
+
+Some examples for how these parameters can be used:
+```python
+# 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})
+
+# Use a filter to only retrieve documents from a specific paper
+docsearch.as_retriever(search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}})
+```
+
+## Return Source Documents
+Additionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.
+
+
+```python
+qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 30}), return_source_documents=True)
+```
+
+
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+result = qa({"query": query})
+```
+
+
+```python
+result["result"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
+```
+
+</CodeOutputBlock>
+
+
+```python
+result["source_documents"]
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    [Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
+     Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
+     Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
+     Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave.  \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]
+```
+
+</CodeOutputBlock>
+
+Alternatively, if our document have a "source" metadata key, we can use the `RetrievalQAWithSourcesChain` to cite our sources:
+
+```python
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))])
+```
+
+```python
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.llms import OpenAI
+
+chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())
+```
+
+```python
+chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
+     'sources': '31-pl'}
+```
+
+</CodeOutputBlock>

docs/docs/use_cases/question_answering/vector_db_text_generation.ipynb

@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve from vector stores directly\n",
+    "\n",
+    "This notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating blog posts that have an understanding of previous blog posts written, or product tutorials that can refer to product documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Data\n",
+    "\n",
+    "First, we prepare the data. For this example, we fetch a documentation site that consists of markdown files hosted on Github and split them into small enough Documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import subprocess\n",
+    "import tempfile\n",
+    "\n",
+    "from langchain.docstore.document import Document\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cloning into '.'...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_github_docs(repo_owner, repo_name):\n",
+    "    with tempfile.TemporaryDirectory() as d:\n",
+    "        subprocess.check_call(\n",
+    "            f\"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .\",\n",
+    "            cwd=d,\n",
+    "            shell=True,\n",
+    "        )\n",
+    "        git_sha = (\n",
+    "            subprocess.check_output(\"git rev-parse HEAD\", shell=True, cwd=d)\n",
+    "            .decode(\"utf-8\")\n",
+    "            .strip()\n",
+    "        )\n",
+    "        repo_path = pathlib.Path(d)\n",
+    "        markdown_files = list(repo_path.glob(\"*/*.md\")) + list(\n",
+    "            repo_path.glob(\"*/*.mdx\")\n",
+    "        )\n",
+    "        for markdown_file in markdown_files:\n",
+    "            with open(markdown_file, \"r\") as f:\n",
+    "                relative_path = markdown_file.relative_to(repo_path)\n",
+    "                github_url = f\"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}\"\n",
+    "                yield Document(page_content=f.read(), metadata={\"source\": github_url})\n",
+    "\n",
+    "\n",
+    "sources = get_github_docs(\"yirenlu92\", \"deno-manual-forked\")\n",
+    "\n",
+    "source_chunks = []\n",
+    "splitter = CharacterTextSplitter(separator=\" \", chunk_size=1024, chunk_overlap=0)\n",
+    "for source in sources:\n",
+    "    for chunk in splitter.split_text(source.page_content):\n",
+    "        source_chunks.append(Document(page_content=chunk, metadata=source.metadata))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Up Vector DB\n",
+    "\n",
+    "Now that we have the documentation content in chunks, let's put all this information in a vector index for easy retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_index = Chroma.from_documents(source_chunks, OpenAIEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Up LLM Chain with Custom Prompt\n",
+    "\n",
+    "Next, let's set up a simple LLM chain but give it a custom prompt for blog post generation. Note that the custom prompt is parameterized and takes two inputs: `context`, which will be the documents fetched from the vector search, and `topic`, which is given by the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "\n",
+    "prompt_template = \"\"\"Use the context below to write a 400 word blog post about the topic below:\n",
+    "    Context: {context}\n",
+    "    Topic: {topic}\n",
+    "    Blog post:\"\"\"\n",
+    "\n",
+    "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"topic\"])\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "chain = LLMChain(llm=llm, prompt=PROMPT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Text\n",
+    "\n",
+    "Finally, we write a function to apply our inputs to the chain. The function takes an input parameter `topic`. We find the documents in the vector index that correspond to that `topic`, and use them as additional context in our simple LLM chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_blog_post(topic):\n",
+    "    docs = search_index.similarity_search(topic, k=4)\n",
+    "    inputs = [{\"context\": doc.page_content, \"topic\": topic} for doc in docs]\n",
+    "    print(chain.apply(inputs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'text': '\\n\\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access environment variables.\\n\\nUsing `Deno.env` is simple. It has getter and setter methods, so you can easily set and retrieve environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain.com\\n```\\n\\nYou can also store environment variables in a `.env` file. This is a great'}, {'text': '\\n\\nEnvironment variables are a powerful tool for managing configuration settings in a program. They allow us to set values that can be used by the program, without having to hard-code them into the code. This makes it easier to change settings without having to modify the code.\\n\\nIn Deno, environment variables can be set in a few different ways. The most common way is to use the `VAR=value` syntax. This will set the environment variable `VAR` to the value `value`. This can be used to set any number of environment variables before running a command. For example, if we wanted to set the environment variable `VAR` to `hello` before running a Deno command, we could do so like this:\\n\\n```\\nVAR=hello deno run main.ts\\n```\\n\\nThis will set the environment variable `VAR` to `hello` before running the command. We can then access this variable in our code using the `Deno.env.get()` function. For example, if we ran the following command:\\n\\n```\\nVAR=hello && deno eval \"console.log(\\'Deno: \\' + Deno.env.get(\\'VAR'}, {'text': '\\n\\nEnvironment variables are a powerful tool for developers, allowing them to store and access data without having to hard-code it into their applications. In Deno, you can access environment variables using the `Deno.env.get()` function.\\n\\nFor example, if you wanted to access the `HOME` environment variable, you could do so like this:\\n\\n```js\\n// env.js\\nDeno.env.get(\"HOME\");\\n```\\n\\nWhen running this code, you\\'ll need to grant the Deno process access to environment variables. This can be done by passing the `--allow-env` flag to the `deno run` command. You can also specify which environment variables you want to grant access to, like this:\\n\\n```shell\\n# Allow access to only the HOME env var\\ndeno run --allow-env=HOME env.js\\n```\\n\\nIt\\'s important to note that environment variables are case insensitive on Windows, so Deno also matches them case insensitively (on Windows only).\\n\\nAnother thing to be aware of when using environment variables is subprocess permissions. Subprocesses are powerful and can access system resources regardless of the permissions you granted to the Den'}, {'text': '\\n\\nEnvironment variables are an important part of any programming language, and Deno is no exception. Deno is a secure JavaScript and TypeScript runtime built on the V8 JavaScript engine, and it recently added support for environment variables. This feature was added in Deno version 1.6.0, and it is now available for use in Deno applications.\\n\\nEnvironment variables are used to store information that can be used by programs. They are typically used to store configuration information, such as the location of a database or the name of a user. In Deno, environment variables are stored in the `Deno.env` object. This object is similar to the `process.env` object in Node.js, and it allows you to access and set environment variables.\\n\\nThe `Deno.env` object is a read-only object, meaning that you cannot directly modify the environment variables. Instead, you must use the `Deno.env.set()` function to set environment variables. This function takes two arguments: the name of the environment variable and the value to set it to. For example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\\n\\n```'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_blog_post(\"environment variables\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docusaurus.config.js

@@ -192,10 +192,6 @@ const config = {
                 to: "/docs/additional_resources/dependents",
                 label: "Dependents",
               },
-              {
-                label: "Integrations Hub",
-                href: "https://integrations.langchain.com/",
-              },
               {
                 to: "/docs/additional_resources/tutorials",
                 label: "Tutorials"

docs/vercel.json

@@ -1,37 +1,5 @@
 {
   "redirects": [
-    {
-      "source": "/docs/use_cases/question_answering/analyze_document",
-      "destination": "/cookbook"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/qa_citations",
-      "destination": "/cookbook"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/chat_vector_db",
-      "destination": "/docs/use_cases/question_answering/"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/in_memory_question_answering",
-      "destination": "/docs/use_cases/question_answering/"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/multi_retrieval_qa_router",
-      "destination": "/docs/use_cases/question_answering/"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/multiple_retrieval",
-      "destination": "/docs/use_cases/question_answering/"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/vector_db_qa",
-      "destination": "/docs/use_cases/question_answering/"
-    },
-    {
-      "source": "/docs/use_cases/question_answering/vector_db_text_generation",
-      "destination": "/docs/use_cases/question_answering/"
-    },
     {
       "source": "/docs/modules/agents/toolkits(/?)",
       "destination": "/docs/modules/agents/tools/toolkits"
@@ -202,7 +170,7 @@
     },
     {
       "source": "/docs/use_cases/question_answering/how_to/chat_vector_db",
-      "destination": "/docs/use_cases/question_answering/"
+      "destination": "/docs/use_cases/question_answering/chat_vector_db"
     },
     {
       "source": "/docs/use_cases/code_understanding",
@@ -234,7 +202,7 @@
     },
     {
       "source": "/docs/use_cases/question_answering/how_to/qa_citations",
-      "destination": "/cookbook"
+      "destination": "/docs/use_cases/question_answering/qa_citations"
     },
     {
       "source": "/docs/use_cases/question_answering/how_to/question_answering",
@@ -3718,11 +3686,11 @@
     },
     {
       "source": "/docs/modules/chains/additional/analyze_document",
-      "destination": "/cookbook"
+      "destination": "/docs/use_cases/question_answering/analyze_document"
     },
     {
       "source": "/docs/modules/chains/popular/chat_vector_db",
-      "destination": "/docs/use_cases/question_answering/"
+      "destination": "/docs/use_cases/question_answering/chat_vector_db"
     },
     {
       "source": "/docs/modules/chains/additional/multi_retrieval_qa_router",
@@ -3862,7 +3830,7 @@
     },
     {
       "source": "/docs/modules/chains/additional/qa_citations",
-      "destination": "/cookbook"
+      "destination": "/docs/use_cases/question_answering/qa_citations"
     },
     {
       "source": "/docs/modules/chains/additional/vector_db_text_generation",

libs/langchain/langchain/agents/load_tools.py

@@ -142,11 +142,7 @@ def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
 
 
 def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
-    chain = APIChain.from_llm_and_api_docs(
-        llm,
-        open_meteo_docs.OPEN_METEO_DOCS,
-        limit_to_domains=["https://api.open-meteo.com/"],
-    )
+    chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
     return Tool(
         name="Open-Meteo-API",
         description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
@@ -163,10 +159,7 @@ _LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] = {
 def _get_news_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
     news_api_key = kwargs["news_api_key"]
     chain = APIChain.from_llm_and_api_docs(
-        llm,
-        news_docs.NEWS_DOCS,
-        headers={"X-Api-Key": news_api_key},
-        limit_to_domains=["https://newsapi.org/"],
+        llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
     )
     return Tool(
         name="News-API",
@@ -181,7 +174,6 @@ def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
         llm,
         tmdb_docs.TMDB_DOCS,
         headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
-        limit_to_domains=["https://api.themoviedb.org/"],
     )
     return Tool(
         name="TMDB-API",
@@ -196,7 +188,6 @@ def _get_podcast_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
         llm,
         podcast_docs.PODCAST_DOCS,
         headers={"X-ListenAPI-Key": listen_api_key},
-        limit_to_domains=["https://listen-api.listennotes.com/"],
     )
     return Tool(
         name="Podcast-API",

libs/langchain/langchain/chat_models/javelin_ai_gateway.py

@@ -1,12 +1,12 @@
 import logging
-from typing import Any, Dict, List, Mapping, Optional, cast
+from typing import Any, Dict, List, Mapping, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
-from langchain.pydantic_v1 import BaseModel, Extra, SecretStr
+from langchain.pydantic_v1 import BaseModel, Extra
 from langchain.schema import (
     ChatGeneration,
     ChatResult,
@@ -65,7 +65,7 @@ class ChatJavelinAIGateway(BaseChatModel):
     client: Any
     """javelin client."""
 
-    javelin_api_key: Optional[SecretStr] = None
+    javelin_api_key: Optional[str] = None
     """The API key for the Javelin AI Gateway."""
 
     def __init__(self, **kwargs: Any):
@@ -84,8 +84,7 @@ class ChatJavelinAIGateway(BaseChatModel):
         if self.gateway_uri:
             try:
                 self.client = JavelinClient(
-                    base_url=self.gateway_uri,
-                    api_key=cast(SecretStr, self.javelin_api_key).get_secret_value(),
+                    base_url=self.gateway_uri, api_key=self.javelin_api_key
                 )
             except UnauthorizedError as e:
                 raise ValueError("Javelin: Incorrect API Key.") from e
@@ -94,7 +93,7 @@ class ChatJavelinAIGateway(BaseChatModel):
     def _default_params(self) -> Dict[str, Any]:
         params: Dict[str, Any] = {
             "gateway_uri": self.gateway_uri,
-            "javelin_api_key": cast(SecretStr, self.javelin_api_key).get_secret_value(),
+            "javelin_api_key": self.javelin_api_key,
             "route": self.route,
             **(self.params.dict() if self.params else {}),
         }

libs/langchain/langchain/chat_models/openai.py

@@ -180,8 +180,8 @@ class ChatOpenAI(BaseChatModel):
         """Return whether this model can be serialized by Langchain."""
         return True
 
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
     model_name: str = Field(default="gpt-3.5-turbo", alias="model")
     """Model name to use."""
     temperature: float = 0.7
@@ -307,17 +307,12 @@ class ChatOpenAI(BaseChatModel):
                 "default_query": values["default_query"],
                 "http_client": values["http_client"],
             }
-
-            if not values.get("client"):
-                values["client"] = openai.OpenAI(**client_params).chat.completions
-            if not values.get("async_client"):
-                values["async_client"] = openai.AsyncOpenAI(
-                    **client_params
-                ).chat.completions
-        elif not values.get("client"):
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.OpenAI(**client_params).chat.completions
+            values["async_client"] = openai.AsyncOpenAI(
+                **client_params
+            ).chat.completions
         else:
-            pass
+            values["client"] = openai.ChatCompletion
         return values
 
     @property

libs/langchain/langchain/document_loaders/notiondb.py

@@ -130,14 +130,6 @@ class NotionDBLoader(BaseLoader):
                 )
             elif prop_type == "created_time":
                 value = prop_data["created_time"] if prop_data["created_time"] else None
-            elif prop_type == "checkbox":
-                value = prop_data["checkbox"]
-            elif prop_type == "email":
-                value = prop_data["email"]
-            elif prop_type == "number":
-                value = prop_data["number"]
-            elif prop_type == "select":
-                value = prop_data["select"]["name"] if prop_data["select"] else None
             else:
                 value = None
 

libs/langchain/langchain/document_loaders/web_base.py

@@ -245,7 +245,7 @@ class WebBaseLoader(BaseLoader):
     def lazy_load(self) -> Iterator[Document]:
         """Lazy load text from the url(s) in web_path."""
         for path in self.web_paths:
-            soup = self._scrape(path, bs_kwargs=self.bs_kwargs)
+            soup = self._scrape(path)
             text = soup.get_text(**self.bs_get_text_kwargs)
             metadata = _build_metadata(soup, path)
             yield Document(page_content=text, metadata=metadata)

libs/langchain/langchain/embeddings/bedrock.py

@@ -112,36 +112,20 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         """Call out to Bedrock embedding endpoint."""
         # replace newlines, which can negatively affect performance.
         text = text.replace(os.linesep, " ")
-
-        # format input body for provider
-        provider = self.model_id.split(".")[0]
         _model_kwargs = self.model_kwargs or {}
-        input_body = {**_model_kwargs}
-        if provider == "cohere":
-            if "input_type" not in input_body.keys():
-                input_body["input_type"] = "search_document"
-            input_body["texts"] = [text]
-        else:
-            # includes common provider == "amazon"
-            input_body["inputText"] = text
+
+        input_body = {**_model_kwargs, "inputText": text}
         body = json.dumps(input_body)
 
         try:
-            # invoke bedrock API
             response = self.client.invoke_model(
                 body=body,
                 modelId=self.model_id,
                 accept="application/json",
                 contentType="application/json",
             )
-
-            # format output based on provider
             response_body = json.loads(response.get("body").read())
-            if provider == "cohere":
-                return response_body.get("embeddings")[0]
-            else:
-                # includes common provider == "amazon"
-                return response_body.get("embedding")
+            return response_body.get("embedding")
         except Exception as e:
             raise ValueError(f"Error raised by inference endpoint: {e}")
 

libs/langchain/langchain/embeddings/openai.py

@@ -175,8 +175,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
     """
 
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
     model: str = "text-embedding-ada-002"
     # to support Azure OpenAI Service custom deployment names
     deployment: Optional[str] = model
@@ -330,16 +330,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                     "default_query": values["default_query"],
                     "http_client": values["http_client"],
                 }
-                if not values.get("client"):
-                    values["client"] = openai.OpenAI(**client_params).embeddings
-                if not values.get("async_client"):
-                    values["async_client"] = openai.AsyncOpenAI(
-                        **client_params
-                    ).embeddings
-            elif not values.get("client"):
-                values["client"] = openai.Embedding
+                values["client"] = openai.OpenAI(**client_params).embeddings
+                values["async_client"] = openai.AsyncOpenAI(**client_params).embeddings
             else:
-                pass
+                values["client"] = openai.Embedding
         return values
 
     @property

libs/langchain/langchain/llms/openai.py

@@ -166,8 +166,8 @@ class BaseOpenAI(BaseLLM):
     def is_lc_serializable(cls) -> bool:
         return True
 
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
     model_name: str = Field(default="text-davinci-003", alias="model")
     """Model name to use."""
     temperature: float = 0.7
@@ -309,14 +309,10 @@ class BaseOpenAI(BaseLLM):
                 "default_query": values["default_query"],
                 "http_client": values["http_client"],
             }
-            if not values.get("client"):
-                values["client"] = openai.OpenAI(**client_params).completions
-            if not values.get("async_client"):
-                values["async_client"] = openai.AsyncOpenAI(**client_params).completions
-        elif not values.get("client"):
-            values["client"] = openai.Completion
+            values["client"] = openai.OpenAI(**client_params).completions
+            values["async_client"] = openai.AsyncOpenAI(**client_params).completions
         else:
-            pass
+            values["client"] = openai.Completion
 
         return values
 
@@ -950,8 +946,10 @@ class OpenAIChat(BaseLLM):
             openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
     """
 
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
+    client: Any  #: :meta private:
+
+    # this is for compatibility with Union types in helper functions
+    async_client: Any  #: :meta private:
     model_name: str = "gpt-3.5-turbo"
     """Model name to use."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)

libs/langchain/langchain/schema/runnable/base.py

@@ -2430,23 +2430,15 @@ class RunnableLambda(Runnable[Input, Output]):
 
     def __repr__(self) -> str:
         """A string representation of this runnable."""
-        if hasattr(self, "func"):
-            return f"RunnableLambda({get_lambda_source(self.func) or '...'})"
-        elif hasattr(self, "afunc"):
-            return f"RunnableLambda(afunc={get_lambda_source(self.afunc) or '...'})"
-        else:
-            return "RunnableLambda(...)"
+        return f"RunnableLambda({get_lambda_source(self.func) or '...'})"
 
     def _invoke(
         self,
         input: Input,
         run_manager: CallbackManagerForChainRun,
         config: RunnableConfig,
-        **kwargs: Any,
     ) -> Output:
-        output = call_func_with_variable_args(
-            self.func, input, config, run_manager, **kwargs
-        )
+        output = call_func_with_variable_args(self.func, input, config, run_manager)
         # If the output is a runnable, invoke it
         if isinstance(output, Runnable):
             recursion_limit = config["recursion_limit"]
@@ -2469,10 +2461,9 @@ class RunnableLambda(Runnable[Input, Output]):
         input: Input,
         run_manager: AsyncCallbackManagerForChainRun,
         config: RunnableConfig,
-        **kwargs: Any,
     ) -> Output:
         output = await acall_func_with_variable_args(
-            self.afunc, input, config, run_manager, **kwargs
+            self.afunc, input, config, run_manager
         )
         # If the output is a runnable, invoke it
         if isinstance(output, Runnable):
@@ -2518,7 +2509,6 @@ class RunnableLambda(Runnable[Input, Output]):
                 self._invoke,
                 input,
                 self._config(config, self.func),
-                **kwargs,
             )
         else:
             raise TypeError(
@@ -2538,7 +2528,6 @@ class RunnableLambda(Runnable[Input, Output]):
                 self._ainvoke,
                 input,
                 self._config(config, self.afunc),
-                **kwargs,
             )
         else:
             # Delegating to super implementation of ainvoke.
@@ -2546,12 +2535,10 @@ class RunnableLambda(Runnable[Input, Output]):
             return await super().ainvoke(input, config)
 
 
-class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
+class RunnableEach(RunnableSerializable[List[Input], List[Output]]):
     """
     A runnable that delegates calls to another runnable
     with each element of the input sequence.
-
-    Use only if creating a new RunnableEach subclass with different __init__ args.
     """
 
     bound: Runnable[Input, Output]
@@ -2602,6 +2589,38 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
     def get_lc_namespace(cls) -> List[str]:
         return cls.__module__.split(".")[:-1]
 
+    def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]:
+        return RunnableEach(bound=self.bound.bind(**kwargs))
+
+    def with_config(
+        self, config: Optional[RunnableConfig] = None, **kwargs: Any
+    ) -> RunnableEach[Input, Output]:
+        return RunnableEach(bound=self.bound.with_config(config, **kwargs))
+
+    def with_listeners(
+        self,
+        *,
+        on_start: Optional[Listener] = None,
+        on_end: Optional[Listener] = None,
+        on_error: Optional[Listener] = None,
+    ) -> RunnableEach[Input, Output]:
+        """
+        Bind lifecycle listeners to a Runnable, returning a new Runnable.
+
+        on_start: Called before the runnable starts running, with the Run object.
+        on_end: Called after the runnable finishes running, with the Run object.
+        on_error: Called if the runnable throws an error, with the Run object.
+
+        The Run object contains information about the run, including its id,
+        type, input, output, error, start_time, end_time, and any tags or metadata
+        added to the run.
+        """
+        return RunnableEach(
+            bound=self.bound.with_listeners(
+                on_start=on_start, on_end=on_end, on_error=on_error
+            )
+        )
+
     def _invoke(
         self,
         inputs: List[Input],
@@ -2635,50 +2654,9 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]):
         return await self._acall_with_config(self._ainvoke, input, config, **kwargs)
 
 
-class RunnableEach(RunnableEachBase[Input, Output]):
-    """
-    A runnable that delegates calls to another runnable
-    with each element of the input sequence.
-    """
-
-    def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]:
-        return RunnableEach(bound=self.bound.bind(**kwargs))
-
-    def with_config(
-        self, config: Optional[RunnableConfig] = None, **kwargs: Any
-    ) -> RunnableEach[Input, Output]:
-        return RunnableEach(bound=self.bound.with_config(config, **kwargs))
-
-    def with_listeners(
-        self,
-        *,
-        on_start: Optional[Listener] = None,
-        on_end: Optional[Listener] = None,
-        on_error: Optional[Listener] = None,
-    ) -> RunnableEach[Input, Output]:
-        """
-        Bind lifecycle listeners to a Runnable, returning a new Runnable.
-
-        on_start: Called before the runnable starts running, with the Run object.
-        on_end: Called after the runnable finishes running, with the Run object.
-        on_error: Called if the runnable throws an error, with the Run object.
-
-        The Run object contains information about the run, including its id,
-        type, input, output, error, start_time, end_time, and any tags or metadata
-        added to the run.
-        """
-        return RunnableEach(
-            bound=self.bound.with_listeners(
-                on_start=on_start, on_end=on_end, on_error=on_error
-            )
-        )
-
-
 class RunnableBindingBase(RunnableSerializable[Input, Output]):
     """
     A runnable that delegates calls to another runnable with a set of kwargs.
-
-    Use only if creating a new RunnableBinding subclass with different __init__ args.
     """
 
     bound: Runnable[Input, Output]
@@ -2901,10 +2879,6 @@ RunnableBindingBase.update_forward_refs(RunnableConfig=RunnableConfig)
 
 
 class RunnableBinding(RunnableBindingBase[Input, Output]):
-    """
-    A runnable that delegates calls to another runnable with a set of kwargs.
-    """
-
     def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
         return self.__class__(
             bound=self.bound,

libs/langchain/langchain/schema/runnable/configurable.py

@@ -34,7 +34,6 @@ from langchain.schema.runnable.utils import (
     Input,
     Output,
     gather_with_concurrency,
-    get_unique_config_specs,
 )
 
 
@@ -210,25 +209,22 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
 
     @property
     def config_specs(self) -> Sequence[ConfigurableFieldSpec]:
-        return get_unique_config_specs(
-            [
-                ConfigurableFieldSpec(
-                    id=spec.id,
-                    name=spec.name,
-                    description=spec.description
-                    or self.default.__fields__[field_name].field_info.description,
-                    annotation=spec.annotation
-                    or self.default.__fields__[field_name].annotation,
-                    default=getattr(self.default, field_name),
-                )
-                if isinstance(spec, ConfigurableField)
-                else make_options_spec(
-                    spec, self.default.__fields__[field_name].field_info.description
-                )
-                for field_name, spec in self.fields.items()
-            ]
-            + list(self.default.config_specs)
-        )
+        return [
+            ConfigurableFieldSpec(
+                id=spec.id,
+                name=spec.name,
+                description=spec.description
+                or self.default.__fields__[field_name].field_info.description,
+                annotation=spec.annotation
+                or self.default.__fields__[field_name].annotation,
+                default=getattr(self.default, field_name),
+            )
+            if isinstance(spec, ConfigurableField)
+            else make_options_spec(
+                spec, self.default.__fields__[field_name].field_info.description
+            )
+            for field_name, spec in self.fields.items()
+        ]
 
     def configurable_fields(
         self, **kwargs: AnyConfigurableField

libs/langchain/langchain/vectorstores/pgvector.py

@@ -147,10 +147,6 @@ class PGVector(VectorStore):
         self.create_tables_if_not_exists()
         self.create_collection()
 
-    def __del__(self) -> None:
-        if self._conn:
-            self._conn.close()
-
     @property
     def embeddings(self) -> Embeddings:
         return self.embedding_function

libs/langchain/poetry.lock

@@ -958,8 +958,8 @@ files = [
 jmespath = ">=0.7.1,<2.0.0"
 python-dateutil = ">=2.1,<3.0.0"
 urllib3 = [
-    {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""},
     {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
+    {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""},
 ]
 
 [package.extras]
@@ -1771,8 +1771,8 @@ files = [
 [package.dependencies]
 aiohttp = ">=3.1.0,<4.0.0"
 grpcio = [
-    {version = ">=1.49.1", markers = "python_version >= \"3.11\""},
     {version = ">=1.22.0", markers = "python_version < \"3.11\""},
+    {version = ">=1.49.1", markers = "python_version >= \"3.11\""},
 ]
 numpy = "*"
 protobuf = ">=3.8.0,<4.0.0"
@@ -2088,20 +2088,23 @@ sqlalchemy = ">=1.3.22"
 
 [[package]]
 name = "duckduckgo-search"
-version = "3.8.5"
+version = "3.9.3"
 description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "duckduckgo_search-3.8.5-py3-none-any.whl", hash = "sha256:9c85190c439f29e95d0cc9509a77d63dbcdbda49a4f9bdf8ff4b567f4a10a44d"},
-    {file = "duckduckgo_search-3.8.5.tar.gz", hash = "sha256:584ea097fa0475cebc278ee464ccd54ba78019dec15a0243723923dc40bc3939"},
+    {file = "duckduckgo_search-3.9.3-py3-none-any.whl", hash = "sha256:4b462333378e9f78e138eccd73a315a54cb5208ebb07ab4ec179d9d18b2998b5"},
+    {file = "duckduckgo_search-3.9.3.tar.gz", hash = "sha256:f68aca605827df4e6b5b4ab00f9a891e103ec30809de092af42e885a617ab5ba"},
 ]
 
 [package.dependencies]
-aiofiles = ">=23.1.0"
-click = ">=8.1.3"
-httpx = {version = ">=0.24.1", extras = ["brotli", "http2", "socks"]}
-lxml = ">=4.9.2"
+aiofiles = ">=23.2.1"
+click = ">=8.1.7"
+httpx = {version = ">=0.25.0", extras = ["brotli", "http2", "socks"]}
+lxml = ">=4.9.3"
+
+[package.extras]
+dev = ["black (>=23.9.1)", "isort (>=5.12.0)", "pytest (>=7.4.2)", "pytest-asyncio (>=0.21.1)", "ruff (>=0.0.291)"]
 
 [[package]]
 name = "elastic-transport"
@@ -2760,8 +2763,8 @@ files = [
 [package.dependencies]
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 proto-plus = [
-    {version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""},
     {version = ">=1.22.0,<2.0.0dev", markers = "python_version < \"3.11\""},
+    {version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""},
 ]
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2932,7 +2935,6 @@ files = [
     {file = "greenlet-3.0.0-cp39-cp39-win32.whl", hash = "sha256:0d3f83ffb18dc57243e0151331e3c383b05e5b6c5029ac29f754745c800f8ed9"},
     {file = "greenlet-3.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:831d6f35037cf18ca5e80a737a27d822d87cd922521d18ed3dbc8a6967be50ce"},
     {file = "greenlet-3.0.0-cp39-universal2-macosx_11_0_x86_64.whl", hash = "sha256:a048293392d4e058298710a54dfaefcefdf49d287cd33fb1f7d63d55426e4355"},
-    {file = "greenlet-3.0.0.tar.gz", hash = "sha256:19834e3f91f485442adc1ee440171ec5d9a4840a1f7bd5ed97833544719ce10b"},
 ]
 
 [package.extras]
@@ -3181,13 +3183,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "0.17.3"
+version = "0.18.0"
 description = "A minimal low-level HTTP client."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "httpcore-0.17.3-py3-none-any.whl", hash = "sha256:c2789b767ddddfa2a5782e3199b2b7f6894540b17b16ec26b2c4d8e103510b87"},
-    {file = "httpcore-0.17.3.tar.gz", hash = "sha256:a6f30213335e34c1ade7be6ec7c47f19f50c56db36abef1a9dfa3815b1cb3888"},
+    {file = "httpcore-0.18.0-py3-none-any.whl", hash = "sha256:adc5398ee0a476567bf87467063ee63584a8bce86078bf748e48754f60202ced"},
+    {file = "httpcore-0.18.0.tar.gz", hash = "sha256:13b5e5cd1dca1a6636a6aaea212b19f4f85cd88c366a2b82304181b769aab3c9"},
 ]
 
 [package.dependencies]
@@ -3216,13 +3218,13 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0
 
 [[package]]
 name = "httpx"
-version = "0.24.1"
+version = "0.25.0"
 description = "The next generation HTTP client."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.24.1-py3-none-any.whl", hash = "sha256:06781eb9ac53cde990577af654bd990a4949de37a28bdb4a230d434f3a30b9bd"},
-    {file = "httpx-0.24.1.tar.gz", hash = "sha256:5853a43053df830c20f8110c5e69fe44d035d850b2dfe795e196f00fdb774bdd"},
+    {file = "httpx-0.25.0-py3-none-any.whl", hash = "sha256:181ea7f8ba3a82578be86ef4171554dd45fec26a02556a744db029a0a27b7100"},
+    {file = "httpx-0.25.0.tar.gz", hash = "sha256:47ecda285389cb32bb2691cc6e069e3ab0205956f681c5b2ad2325719751d875"},
 ]
 
 [package.dependencies]
@@ -3230,7 +3232,7 @@ brotli = {version = "*", optional = true, markers = "platform_python_implementat
 brotlicffi = {version = "*", optional = true, markers = "platform_python_implementation != \"CPython\" and extra == \"brotli\""}
 certifi = "*"
 h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
-httpcore = ">=0.15.0,<0.18.0"
+httpcore = ">=0.18.0,<0.19.0"
 idna = "*"
 sniffio = "*"
 socksio = {version = "==1.*", optional = true, markers = "extra == \"socks\""}
@@ -3535,21 +3537,6 @@ files = [
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
-[[package]]
-name = "javelin-sdk"
-version = "0.1.8"
-description = "Python client for Javelin"
-optional = true
-python-versions = ">=3.8,<4.0"
-files = [
-    {file = "javelin_sdk-0.1.8-py3-none-any.whl", hash = "sha256:7843e278f99fa04fcc659b31844f6205141b956e24f331a1cac1ae30d9eb3a55"},
-    {file = "javelin_sdk-0.1.8.tar.gz", hash = "sha256:57fa669c68f75296fdce20242023429a79755be22e0d3182dbad62d8f6bb1dd7"},
-]
-
-[package.dependencies]
-httpx = ">=0.24.0,<0.25.0"
-pydantic = ">=1.10.7,<2.0.0"
-
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -3833,13 +3820,13 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.5.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.5.0-py3-none-any.whl", hash = "sha256:c3877aac7257ec68d79b5c622ce986bd2a992ca42f6ddc9b4dd1da50e89f7028"},
+    {file = "jupyter_client-8.5.0.tar.gz", hash = "sha256:e8754066510ce456358df363f97eae64b50860f30dc1fe8c6771440db3be9a63"},
 ]
 
 [package.dependencies]
@@ -5736,25 +5723,25 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.2.4"
-description = "The official Python library for the openai API"
+version = "0.27.10"
+description = "Python client library for the OpenAI API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.2.4-py3-none-any.whl", hash = "sha256:53927a2ca276eec0a0efdc1ae829f74a51f49b7d3e14cc6f820aeafb0abfd802"},
-    {file = "openai-1.2.4.tar.gz", hash = "sha256:d99a474049376be431d9b4dec3a5c895dd76e19165748c5944e80b7905d1b1ff"},
+    {file = "openai-0.27.10-py3-none-any.whl", hash = "sha256:beabd1757e3286fa166dde3b70ebb5ad8081af046876b47c14c41e203ed22a14"},
+    {file = "openai-0.27.10.tar.gz", hash = "sha256:60e09edf7100080283688748c6803b7b3b52d5a55d21890f3815292a0552d83b"},
 ]
 
 [package.dependencies]
-anyio = ">=3.5.0,<4"
-distro = ">=1.7.0,<2"
-httpx = ">=0.23.0,<1"
-pydantic = ">=1.9.0,<3"
-tqdm = ">4"
-typing-extensions = ">=4.5,<5"
+aiohttp = "*"
+requests = ">=2.20"
+tqdm = "*"
 
 [package.extras]
-datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+dev = ["black (>=21.6b0,<22.0)", "pytest (==6.*)", "pytest-asyncio", "pytest-mock"]
+embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"]
+wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
 
 [[package]]
 name = "openapi-pydantic"
@@ -5788,12 +5775,12 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
-    {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
 ]
 
 [[package]]
@@ -5969,8 +5956,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
@@ -6482,6 +6469,8 @@ files = [
     {file = "psycopg2-2.9.9-cp310-cp310-win_amd64.whl", hash = "sha256:426f9f29bde126913a20a96ff8ce7d73fd8a216cfb323b1f04da402d452853c3"},
     {file = "psycopg2-2.9.9-cp311-cp311-win32.whl", hash = "sha256:ade01303ccf7ae12c356a5e10911c9e1c51136003a9a1d92f7aa9d010fb98372"},
     {file = "psycopg2-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:121081ea2e76729acfb0673ff33755e8703d45e926e416cb59bae3a86c6a4981"},
+    {file = "psycopg2-2.9.9-cp312-cp312-win32.whl", hash = "sha256:d735786acc7dd25815e89cc4ad529a43af779db2e25aa7c626de864127e5a024"},
+    {file = "psycopg2-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win32.whl", hash = "sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win_amd64.whl", hash = "sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a"},
     {file = "psycopg2-2.9.9-cp38-cp38-win32.whl", hash = "sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"},
@@ -6524,6 +6513,7 @@ files = [
     {file = "psycopg2_binary-2.9.9-cp311-cp311-win32.whl", hash = "sha256:dc4926288b2a3e9fd7b50dc6a1909a13bbdadfc67d93f3374d984e56f885579d"},
     {file = "psycopg2_binary-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:b76bedd166805480ab069612119ea636f5ab8f8771e640ae103e05a4aae3e417"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8532fd6e6e2dc57bcb3bc90b079c60de896d2128c5d9d6f24a63875a95a088cf"},
+    {file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b0605eaed3eb239e87df0d5e3c6489daae3f7388d455d0c0b4df899519c6a38d"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f8544b092a29a6ddd72f3556a9fcf249ec412e10ad28be6a0c0d948924f2212"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2d423c8d8a3c82d08fe8af900ad5b613ce3632a1249fd6a223941d0735fce493"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e5afae772c00980525f6d6ecf7cbca55676296b580c0e6abb407f15f3706996"},
@@ -6532,6 +6522,8 @@ files = [
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:cb16c65dcb648d0a43a2521f2f0a2300f40639f6f8c1ecbc662141e4e3e1ee07"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:911dda9c487075abd54e644ccdf5e5c16773470a6a5d3826fda76699410066fb"},
     {file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:57fede879f08d23c85140a360c6a77709113efd1c993923c59fde17aa27599fe"},
+    {file = "psycopg2_binary-2.9.9-cp312-cp312-win32.whl", hash = "sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93"},
+    {file = "psycopg2_binary-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:81ff62668af011f9a48787564ab7eded4e9fb17a4a6a74af5ffa6a457400d2ab"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:2293b001e319ab0d869d660a704942c9e2cce19745262a8aba2115ef41a0a42a"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03ef7df18daf2c4c07e2695e8cfd5ee7f748a1d54d802330985a78d2a5a6dca9"},
     {file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a602ea5aff39bb9fac6308e9c9d82b9a35c2bf288e184a816002c9fae930b77"},
@@ -11037,7 +11029,7 @@ cli = ["typer"]
 cohere = ["cohere"]
 docarray = ["docarray"]
 embeddings = ["sentence-transformers"]
-extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "dashvector", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "html2text", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openai", "openapi-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict"]
+extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "dashvector", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "html2text", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openai", "openapi-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict"]
 javascript = ["esprima"]
 llms = ["clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"]
 openai = ["openai", "tiktoken"]
@@ -11047,4 +11039,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "964150ae1b32757b20d1eafa4cdf46f1dc0273fd8879f2ee3229642339df2cd5"
+content-hash = "081a3172b585398f728ddaf7316fb3f11bc647e3d3bcc5071691add5413a636e"

libs/langchain/pyproject.toml

@@ -46,7 +46,7 @@ dataclasses-json = ">= 0.5.7, < 0.7"
 tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"}
 tenacity = "^8.1.0"
 cohere = {version = "^4", optional = true}
-openai = {version = "<2", optional = true}
+openai = {version = "^0", optional = true}
 nlpcloud = {version = "^1", optional = true}
 nomic = {version = "^1.0.43", optional = true}
 huggingface_hub = {version = "^0", optional = true}
@@ -140,9 +140,6 @@ rspace_client = {version = "^2.5.0", optional = true}
 upstash-redis = {version = "^0.15.0", optional = true}
 google-cloud-documentai = {version = "^2.20.1", optional = true}
 fireworks-ai = {version = "^0.6.0", optional = true, python = ">=3.9,<4.0"}
-javelin-sdk = {version = "^0.1.8", optional = true}
-asyncpg = {version = "^0.28.0", optional = true}
-greenlet = {version = "^2.0.2", optional = true}
 
 
 [tool.poetry.group.test.dependencies]
@@ -189,7 +186,7 @@ optional = true
 # https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md#working-with-optional-dependencies
 pytest-vcr = "^1.0.2"
 wrapt = "^1.15.0"
-openai = "^1"
+openai = "^0.27.4"
 python-dotenv = "^1.0.0"
 cassio = "^0.1.0"
 tiktoken = "^0.3.2"
@@ -369,8 +366,6 @@ extended_testing = [
  "arxiv",
  "dashvector",
  "sqlite-vss",
- "asyncpg",
- "greenlet",
  "rapidocr-onnxruntime",
  "motor",
  "timescale-vector",
@@ -378,7 +373,6 @@ extended_testing = [
  "upstash-redis",
  "rspace_client",
  "fireworks-ai",
- "javelin-sdk",
 ]
 
 [tool.ruff]

libs/langchain/tests/integration_tests/vectorstores/test_pgvector_async.py

@@ -1,370 +0,0 @@
-"""Test PGVector functionality."""
-import os
-from contextlib import asynccontextmanager
-from typing import List
-
-import pytest
-from sqlalchemy import select
-
-from langchain.docstore.document import Document
-from langchain.vectorstores.pgvector_async import PGVectorAsync
-from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
-
-DRIVER = os.environ.get("TEST_PGVECTOR_DRIVER", "asyncpg")
-HOST = os.environ.get("TEST_PGVECTOR_HOST", "localhost")
-PORT = int(os.environ.get("TEST_PGVECTOR_PORT", "5432"))
-DATABASE = os.environ.get("TEST_PGVECTOR_DATABASE", "postgres")
-USER = os.environ.get("TEST_PGVECTOR_USER", "postgres")
-PASSWORD = os.environ.get("TEST_PGVECTOR_PASSWORD", "postgres")
-
-DATABASE_URL = f"postgresql+{DRIVER}://{USER}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}"
-
-ADA_TOKEN_COUNT = 1536
-
-
-class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
-    """Fake embeddings functionality for testing."""
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Return simple embeddings."""
-        return [
-            [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
-        ]
-
-    def embed_query(self, text: str) -> List[float]:
-        """Return simple embeddings."""
-        return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
-
-
-@asynccontextmanager
-async def with_db():
-    vectorstore = PGVectorAsync(
-        collection_name="test_collection",
-        embeddings=FakeEmbeddingsWithAdaDimension(),
-        db_url=DATABASE_URL,
-    )
-
-    await vectorstore.drop_schema()
-    await vectorstore.create_schema()
-    yield
-    await vectorstore.drop_schema()
-
-
-@pytest.mark.asyncio
-async def test_pgvector() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search("foo", k=1)
-        assert output == [Document(page_content="foo")]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_embeddings() -> None:
-    """Test end to end construction with embeddings and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
-        text_embedding_pairs = list(zip(texts, text_embeddings))
-        docsearch = await PGVectorAsync.afrom_embeddings(
-            text_embeddings=text_embedding_pairs,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search("foo", k=1)
-        assert output == [Document(page_content="foo")]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_metadatas() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search("foo", k=1)
-        assert output == [Document(page_content="foo", metadata={"page": "0"})]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_metadatas_with_scores() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search_with_score("foo", k=1)
-        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_filter_match() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection_filter",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search_with_score(
-            "foo", k=1, filter={"page": "0"}
-        )
-        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_filter_distant_match() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection_filter",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search_with_score(
-            "foo", k=1, filter={"page": "2"}
-        )
-        assert output == [
-            (
-                Document(page_content="baz", metadata={"page": "2"}),
-                0.0013003906671379406,
-            )
-        ]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_filter_no_match() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection_filter",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search_with_score(
-            "foo", k=1, filter={"page": "5"}
-        )
-        assert output == []
-
-
-@pytest.mark.asyncio
-async def test_pgvector_collection_with_metadata() -> None:
-    """Test end to end collection construction"""
-    async with with_db():
-        pgvector = PGVectorAsync(
-            collection_name="test_collection",
-            collection_metadata={"foo": "bar"},
-            embeddings=FakeEmbeddingsWithAdaDimension(),
-            db_url=DATABASE_URL,
-        )
-        await pgvector.delete_collection()  # Delete collection if it exists
-        await pgvector.create_collection()
-        collection = await pgvector.get_collection()
-        if collection is None:
-            assert False, "Expected a CollectionStore object but received None"
-        else:
-            assert collection.name == "test_collection"
-            assert collection.cmetadata == {"foo": "bar"}
-
-
-@pytest.mark.asyncio
-async def test_pgvector_with_filter_in_set() -> None:
-    """Test end to end construction and search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection_filter",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.asimilarity_search_with_score(
-            "foo", k=2, filter={"page": {"IN": ["0", "2"]}}
-        )
-        assert output == [
-            (Document(page_content="foo", metadata={"page": "0"}), 0.0),
-            (
-                Document(page_content="baz", metadata={"page": "2"}),
-                0.0013003906671379406,
-            ),
-        ]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_delete_docs() -> None:
-    """Add and delete documents."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection_filter",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            ids=["1", "2", "3"],
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        await docsearch.adelete(["1", "2"])
-        async with docsearch._make_session() as session:
-            query = select(docsearch.EmbeddingStore)
-            results = await session.execute(query)
-            records = list(results.scalars().all())
-
-            assert sorted(record.custom_id for record in records) == ["3"]
-
-        await docsearch.adelete(["2", "3"])  # Should not raise on missing ids
-        async with docsearch._make_session() as session:
-            query = select(docsearch.EmbeddingStore)
-            results = await session.execute(query)
-            records = list(results.scalars().all())
-
-            assert sorted(record.custom_id for record in records) == []  # type: ignore
-
-
-@pytest.mark.asyncio
-async def test_pgvector_relevance_score() -> None:
-    """Test to make sure the relevance score is scaled to 0-1."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-
-        output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3)
-        assert output == [
-            (Document(page_content="foo", metadata={"page": "0"}), 1.0),
-            (Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
-            (Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
-        ]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_retriever_search_threshold() -> None:
-    """Test using retriever for searching with threshold."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-
-        retriever = docsearch.as_retriever(
-            search_type="similarity_score_threshold",
-            search_kwargs={"k": 3, "score_threshold": 0.999},
-        )
-        output = await retriever.aget_relevant_documents("summer")
-        assert output == [
-            Document(page_content="foo", metadata={"page": "0"}),
-            Document(page_content="bar", metadata={"page": "1"}),
-        ]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_retriever_search_threshold_custom_normalization_fn() -> None:
-    """Test searching with threshold and custom normalization function"""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        metadatas = [{"page": str(i)} for i in range(len(texts))]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            metadatas=metadatas,
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-            relevance_score_fn=lambda d: d * 0,
-        )
-
-        retriever = docsearch.as_retriever(
-            search_type="similarity_score_threshold",
-            search_kwargs={"k": 3, "score_threshold": 0.5},
-        )
-        output = await retriever.aget_relevant_documents("foo")
-        assert output == []
-
-
-@pytest.mark.asyncio
-async def test_pgvector_max_marginal_relevance_search() -> None:
-    """Test max marginal relevance search."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.amax_marginal_relevance_search("foo", k=1, fetch_k=3)
-        assert output == [Document(page_content="foo")]
-
-
-@pytest.mark.asyncio
-async def test_pgvector_max_marginal_relevance_search_with_score() -> None:
-    """Test max marginal relevance search with relevance scores."""
-    async with with_db():
-        texts = ["foo", "bar", "baz"]
-        docsearch = await PGVectorAsync.afrom_texts(
-            texts=texts,
-            collection_name="test_collection",
-            embedding=FakeEmbeddingsWithAdaDimension(),
-            db_url=DATABASE_URL,
-            pre_delete_collection=True,
-        )
-        output = await docsearch.amax_marginal_relevance_search_with_score(
-            "foo", k=1, fetch_k=3
-        )
-        assert output == [(Document(page_content="foo"), 0.0)]

libs/langchain/tests/unit_tests/chat_models/test_javelin_ai_gateway.py

@@ -1,32 +0,0 @@
-"""Test `Javelin AI Gateway` chat models"""
-
-import pytest
-
-from langchain.chat_models import ChatJavelinAIGateway
-from langchain.pydantic_v1 import SecretStr
-
-
-@pytest.mark.requires("javelin_sdk")
-def test_api_key_is_secret_string() -> None:
-    llm = ChatJavelinAIGateway(
-        gateway_uri="<javelin-ai-gateway-uri>",
-        route="<javelin-ai-gateway-chat-route>",
-        javelin_api_key="secret-api-key",
-        params={"temperature": 0.1},
-    )
-    assert isinstance(llm.javelin_api_key, SecretStr)
-    assert llm.javelin_api_key.get_secret_value() == "secret-api-key"
-
-
-@pytest.mark.requires("javelin_sdk")
-def test_api_key_masked_when_passed_via_constructor() -> None:
-    llm = ChatJavelinAIGateway(
-        gateway_uri="<javelin-ai-gateway-uri>",
-        route="<javelin-ai-gateway-chat-route>",
-        javelin_api_key="secret-api-key",
-        params={"temperature": 0.1},
-    )
-
-    assert str(llm.javelin_api_key) == "**********"
-    assert "secret-api-key" not in repr(llm.javelin_api_key)
-    assert "secret-api-key" not in repr(llm)

libs/langchain/tests/unit_tests/llms/test_gooseai.py

@@ -5,14 +5,6 @@ from pytest import MonkeyPatch
 
 from langchain.llms.gooseai import GooseAI
 from langchain.pydantic_v1 import SecretStr
-from langchain.utils.openai import is_openai_v1
-
-
-def _openai_v1_installed() -> bool:
-    try:
-        return is_openai_v1()
-    except Exception as _:
-        return False
 
 
 @pytest.mark.requires("openai")
@@ -22,9 +14,6 @@ def test_api_key_is_secret_string() -> None:
     assert llm.gooseai_api_key.get_secret_value() == "secret-api-key"
 
 
-@pytest.mark.skipif(
-    _openai_v1_installed(), reason="GooseAI currently only works with openai<1"
-)
 @pytest.mark.requires("openai")
 def test_api_key_masked_when_passed_via_constructor() -> None:
     llm = GooseAI(gooseai_api_key="secret-api-key")
@@ -33,9 +22,6 @@ def test_api_key_masked_when_passed_via_constructor() -> None:
     assert "secret-api-key" not in repr(llm)
 
 
-@pytest.mark.skipif(
-    _openai_v1_installed(), reason="GooseAI currently only works with openai<1"
-)
 @pytest.mark.requires("openai")
 def test_api_key_masked_when_passed_from_env() -> None:
     with MonkeyPatch.context() as mp:

libs/langchain/tests/unit_tests/llms/test_openai.py

@@ -8,7 +8,6 @@ from tenacity import wait_none
 
 from langchain.llms import base
 from langchain.llms.openai import OpenAI
-from langchain.utils.openai import is_openai_v1
 from tests.unit_tests.callbacks.fake_callback_handler import (
     FakeAsyncCallbackHandler,
     FakeCallbackHandler,
@@ -17,13 +16,6 @@ from tests.unit_tests.callbacks.fake_callback_handler import (
 os.environ["OPENAI_API_KEY"] = "foo"
 
 
-def _openai_v1_installed() -> bool:
-    try:
-        return is_openai_v1()
-    except Exception as _:
-        return False
-
-
 @pytest.mark.requires("openai")
 def test_openai_model_param() -> None:
     llm = OpenAI(model="foo")
@@ -75,9 +67,6 @@ def _patched_retry(*args: Any, **kwargs: Any) -> Any:
     return r
 
 
-@pytest.mark.skipif(
-    _openai_v1_installed(), reason="Retries only handled by LangChain for openai<1"
-)
 @pytest.mark.requires("openai")
 def test_openai_retries(mock_completion: dict) -> None:
     llm = OpenAI()
@@ -111,9 +100,6 @@ def test_openai_retries(mock_completion: dict) -> None:
     assert callback_handler.retries == 1
 
 
-@pytest.mark.skipif(
-    _openai_v1_installed(), reason="Retries only handled by LangChain for openai<1"
-)
 @pytest.mark.requires("openai")
 @pytest.mark.asyncio
 async def test_openai_async_retries(mock_completion: dict) -> None:

libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr

@@ -226,6 +226,17 @@
             "id": [
               "OPENAI_API_KEY"
             ]
+          },
+          "client": {
+            "lc": 1,
+            "type": "not_implemented",
+            "id": [
+              "openai",
+              "api_resources",
+              "completion",
+              "Completion"
+            ],
+            "repr": "<class 'openai.api_resources.completion.Completion'>"
           }
         }
       },

libs/langchain/tests/unit_tests/load/test_load.py

@@ -69,7 +69,7 @@ def test_loads_llmchain_with_non_serializable_arg() -> None:
         model="davinci",
         temperature=0.5,
         openai_api_key="hello",
-        http_client=NotSerializable,
+        client=NotSerializable,
     )
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)
@@ -134,7 +134,7 @@ def test_load_llmchain_with_non_serializable_arg() -> None:
         model="davinci",
         temperature=0.5,
         openai_api_key="hello",
-        http_client=NotSerializable,
+        client=NotSerializable,
     )
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)

templates/rag-self-query/poetry.lock

@@ -323,13 +323,13 @@ files = [
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.2"
+version = "0.6.1"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "dataclasses_json-0.6.2-py3-none-any.whl", hash = "sha256:71816ced3d0f55a2c5bc1a813ace1b8d4234e79a08744269a7cf84d6f7c06e99"},
-    {file = "dataclasses_json-0.6.2.tar.gz", hash = "sha256:1b934c1bd63e775880946b8361a902d7de86e894bab8098eab27c010f95724d1"},
+    {file = "dataclasses_json-0.6.1-py3-none-any.whl", hash = "sha256:1bd8418a61fe3d588bb0079214d7fb71d44937da40742b787256fd53b26b6c80"},
+    {file = "dataclasses_json-0.6.1.tar.gz", hash = "sha256:a53c220c35134ce08211a1057fd0e5bf76dc5331627c6b241cacbc570a89faae"},
 ]
 
 [package.dependencies]
@@ -356,13 +356,13 @@ develop = ["aiohttp", "furo", "mock", "pytest", "pytest-asyncio", "pytest-cov",
 
 [[package]]
 name = "elasticsearch"
-version = "8.11.0"
+version = "8.10.1"
 description = "Python client for Elasticsearch"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.6, <4"
 files = [
-    {file = "elasticsearch-8.11.0-py3-none-any.whl", hash = "sha256:26b72957ee617c9f0b23ac872e1c133cf9d7f5d439c615daaa11016265da36ab"},
-    {file = "elasticsearch-8.11.0.tar.gz", hash = "sha256:9e08413beaff3a46bc10c6c57069a84704df6aaa93085c737df07f58a2811b78"},
+    {file = "elasticsearch-8.10.1-py3-none-any.whl", hash = "sha256:68141d42d10c7f67ac466ca00496830d3b81a7e9476c3baa5585060832c60c69"},
+    {file = "elasticsearch-8.10.1.tar.gz", hash = "sha256:2cb56b433daa2d3ef1aaa2e5a5eacd36ba1d66884722f3d7759a4f9d16190059"},
 ]
 
 [package.dependencies]
@@ -877,13 +877,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.0.335"
+version = "0.0.327"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.335-py3-none-any.whl", hash = "sha256:f74c98366070a46953c071c69f6c01671a9437569c08406cace256ccaabdfcaf"},
-    {file = "langchain-0.0.335.tar.gz", hash = "sha256:93136fe6cc9ac06a80ccf7cf581e58af5cfcc31fef1083b30165df9a9bc53f5d"},
+    {file = "langchain-0.0.327-py3-none-any.whl", hash = "sha256:21835600e1ab11e2a939d9e473c13ed51402a3b75418ca02689877a5764da398"},
+    {file = "langchain-0.0.327.tar.gz", hash = "sha256:2710fba0c0735d1a63327cad83387571adc457fe75075c70335e8ea628f0a8a2"},
 ]
 
 [package.dependencies]
@@ -892,7 +892,7 @@ anyio = "<4.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.0.63,<0.1.0"
+langsmith = ">=0.0.52,<0.1.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -935,21 +935,20 @@ uvicorn = ">=0.23.2,<0.24.0"
 
 [[package]]
 name = "langserve"
-version = "0.0.27"
+version = "0.0.24"
 description = ""
 optional = false
 python-versions = ">=3.8.1,<4.0.0"
 files = [
-    {file = "langserve-0.0.27-py3-none-any.whl", hash = "sha256:7d9af0dc96edad243f22b60bfa155aaf324164f3e608bd3006e97180a40d4770"},
-    {file = "langserve-0.0.27.tar.gz", hash = "sha256:c8ad5acf3c2d46ceea66b3c85615732b267cefbfc0924267f2326db9bb019736"},
+    {file = "langserve-0.0.24-py3-none-any.whl", hash = "sha256:7c3f65b2585f39e5ad0b4ae8609eac9b59079ed5a7cfd8ef96d20a3ee5c20309"},
+    {file = "langserve-0.0.24.tar.gz", hash = "sha256:aba25983943b2408a8bdf2d7a324da58d3ab84ce4ea6b134bc5ccd969e522725"},
 ]
 
 [package.dependencies]
 fastapi = {version = ">=0.90.1", optional = true, markers = "extra == \"server\" or extra == \"all\""}
 httpx = ">=0.23.0"
 httpx-sse = {version = ">=0.3.1", optional = true, markers = "extra == \"client\" or extra == \"all\""}
-langchain = ">=0.0.333"
-orjson = ">=2"
+langchain = ">=0.0.322"
 pydantic = ">=1"
 sse-starlette = {version = ">=1.3.0,<2.0.0", optional = true, markers = "extra == \"server\" or extra == \"all\""}
 
@@ -960,13 +959,13 @@ server = ["fastapi (>=0.90.1)", "sse-starlette (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.0.64"
+version = "0.0.56"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.64-py3-none-any.whl", hash = "sha256:461acdcd8332d1325c16dc57e8a2d5ec9d1578490a4eaabe14db74db74ceaf21"},
-    {file = "langsmith-0.0.64.tar.gz", hash = "sha256:e78c02501c2cff24fff7bd2d28ff3765b21675c7f0fcf6a09932bc218603c36e"},
+    {file = "langsmith-0.0.56-py3-none-any.whl", hash = "sha256:5aed1ad2395700442a6511651eca17d60eff56878f18bdd9e1d20b3c6f7e016c"},
+    {file = "langsmith-0.0.56.tar.gz", hash = "sha256:98382931f61a984a3d02cad07e4b986a0a7c843f87830172692c987deb8ba554"},
 ]
 
 [package.dependencies]
@@ -1317,65 +1316,6 @@ dev = ["black (>=21.6b0,<22.0)", "pytest (==6.*)", "pytest-asyncio", "pytest-moc
 embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"]
 wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
 
-[[package]]
-name = "orjson"
-version = "3.9.10"
-description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "orjson-3.9.10-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:c18a4da2f50050a03d1da5317388ef84a16013302a5281d6f64e4a3f406aabc4"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5148bab4d71f58948c7c39d12b14a9005b6ab35a0bdf317a8ade9a9e4d9d0bd5"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4cf7837c3b11a2dfb589f8530b3cff2bd0307ace4c301e8997e95c7468c1378e"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c62b6fa2961a1dcc51ebe88771be5319a93fd89bd247c9ddf732bc250507bc2b"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:deeb3922a7a804755bbe6b5be9b312e746137a03600f488290318936c1a2d4dc"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1234dc92d011d3554d929b6cf058ac4a24d188d97be5e04355f1b9223e98bbe9"},
-    {file = "orjson-3.9.10-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:06ad5543217e0e46fd7ab7ea45d506c76f878b87b1b4e369006bdb01acc05a83"},
-    {file = "orjson-3.9.10-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4fd72fab7bddce46c6826994ce1e7de145ae1e9e106ebb8eb9ce1393ca01444d"},
-    {file = "orjson-3.9.10-cp310-none-win32.whl", hash = "sha256:b5b7d4a44cc0e6ff98da5d56cde794385bdd212a86563ac321ca64d7f80c80d1"},
-    {file = "orjson-3.9.10-cp310-none-win_amd64.whl", hash = "sha256:61804231099214e2f84998316f3238c4c2c4aaec302df12b21a64d72e2a135c7"},
-    {file = "orjson-3.9.10-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:cff7570d492bcf4b64cc862a6e2fb77edd5e5748ad715f487628f102815165e9"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed8bc367f725dfc5cabeed1ae079d00369900231fbb5a5280cf0736c30e2adf7"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c812312847867b6335cfb264772f2a7e85b3b502d3a6b0586aa35e1858528ab1"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9edd2856611e5050004f4722922b7b1cd6268da34102667bd49d2a2b18bafb81"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:674eb520f02422546c40401f4efaf8207b5e29e420c17051cddf6c02783ff5ca"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d0dc4310da8b5f6415949bd5ef937e60aeb0eb6b16f95041b5e43e6200821fb"},
-    {file = "orjson-3.9.10-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e99c625b8c95d7741fe057585176b1b8783d46ed4b8932cf98ee145c4facf499"},
-    {file = "orjson-3.9.10-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:ec6f18f96b47299c11203edfbdc34e1b69085070d9a3d1f302810cc23ad36bf3"},
-    {file = "orjson-3.9.10-cp311-none-win32.whl", hash = "sha256:ce0a29c28dfb8eccd0f16219360530bc3cfdf6bf70ca384dacd36e6c650ef8e8"},
-    {file = "orjson-3.9.10-cp311-none-win_amd64.whl", hash = "sha256:cf80b550092cc480a0cbd0750e8189247ff45457e5a023305f7ef1bcec811616"},
-    {file = "orjson-3.9.10-cp312-cp312-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:602a8001bdf60e1a7d544be29c82560a7b49319a0b31d62586548835bbe2c862"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f295efcd47b6124b01255d1491f9e46f17ef40d3d7eabf7364099e463fb45f0f"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:92af0d00091e744587221e79f68d617b432425a7e59328ca4c496f774a356071"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c5a02360e73e7208a872bf65a7554c9f15df5fe063dc047f79738998b0506a14"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:858379cbb08d84fe7583231077d9a36a1a20eb72f8c9076a45df8b083724ad1d"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666c6fdcaac1f13eb982b649e1c311c08d7097cbda24f32612dae43648d8db8d"},
-    {file = "orjson-3.9.10-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3fb205ab52a2e30354640780ce4587157a9563a68c9beaf52153e1cea9aa0921"},
-    {file = "orjson-3.9.10-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:7ec960b1b942ee3c69323b8721df2a3ce28ff40e7ca47873ae35bfafeb4555ca"},
-    {file = "orjson-3.9.10-cp312-none-win_amd64.whl", hash = "sha256:3e892621434392199efb54e69edfff9f699f6cc36dd9553c5bf796058b14b20d"},
-    {file = "orjson-3.9.10-cp38-cp38-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:8b9ba0ccd5a7f4219e67fbbe25e6b4a46ceef783c42af7dbc1da548eb28b6531"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e2ecd1d349e62e3960695214f40939bbfdcaeaaa62ccc638f8e651cf0970e5f"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7f433be3b3f4c66016d5a20e5b4444ef833a1f802ced13a2d852c637f69729c1"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4689270c35d4bb3102e103ac43c3f0b76b169760aff8bcf2d401a3e0e58cdb7f"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4bd176f528a8151a6efc5359b853ba3cc0e82d4cd1fab9c1300c5d957dc8f48c"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a2ce5ea4f71681623f04e2b7dadede3c7435dfb5e5e2d1d0ec25b35530e277b"},
-    {file = "orjson-3.9.10-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:49f8ad582da6e8d2cf663c4ba5bf9f83cc052570a3a767487fec6af839b0e777"},
-    {file = "orjson-3.9.10-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:2a11b4b1a8415f105d989876a19b173f6cdc89ca13855ccc67c18efbd7cbd1f8"},
-    {file = "orjson-3.9.10-cp38-none-win32.whl", hash = "sha256:a353bf1f565ed27ba71a419b2cd3db9d6151da426b61b289b6ba1422a702e643"},
-    {file = "orjson-3.9.10-cp38-none-win_amd64.whl", hash = "sha256:e28a50b5be854e18d54f75ef1bb13e1abf4bc650ab9d635e4258c58e71eb6ad5"},
-    {file = "orjson-3.9.10-cp39-cp39-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:ee5926746232f627a3be1cc175b2cfad24d0170d520361f4ce3fa2fd83f09e1d"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a73160e823151f33cdc05fe2cea557c5ef12fdf276ce29bb4f1c571c8368a60"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c338ed69ad0b8f8f8920c13f529889fe0771abbb46550013e3c3d01e5174deef"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5869e8e130e99687d9e4be835116c4ebd83ca92e52e55810962446d841aba8de"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d2c1e559d96a7f94a4f581e2a32d6d610df5840881a8cba8f25e446f4d792df3"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81a3a3a72c9811b56adf8bcc829b010163bb2fc308877e50e9910c9357e78521"},
-    {file = "orjson-3.9.10-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7f8fb7f5ecf4f6355683ac6881fd64b5bb2b8a60e3ccde6ff799e48791d8f864"},
-    {file = "orjson-3.9.10-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c943b35ecdf7123b2d81d225397efddf0bce2e81db2f3ae633ead38e85cd5ade"},
-    {file = "orjson-3.9.10-cp39-none-win32.whl", hash = "sha256:fb0b361d73f6b8eeceba47cd37070b5e6c9de5beaeaa63a1cb35c7e1a73ef088"},
-    {file = "orjson-3.9.10-cp39-none-win_amd64.whl", hash = "sha256:b90f340cb6397ec7a854157fac03f0c82b744abdd1c0941a024c3c29d1340aff"},
-    {file = "orjson-3.9.10.tar.gz", hash = "sha256:9ebbdbd6a046c304b1845e96fbcc5559cd296b4dfd3ad2509e33c4d9ce07d6a1"},
-]
-
 [[package]]
 name = "packaging"
 version = "23.2"
@@ -1456,18 +1396,18 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
 [[package]]
 name = "pydantic"
-version = "2.5.0"
+version = "2.4.2"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pydantic-2.5.0-py3-none-any.whl", hash = "sha256:7ce6e766c456ad026fe5712f7bcf036efc34bd5d107b3e669ef7ea01b3a9050c"},
-    {file = "pydantic-2.5.0.tar.gz", hash = "sha256:69bd6fb62d2d04b7055f59a396993486a2ee586c43a0b89231ce0000de07627c"},
+    {file = "pydantic-2.4.2-py3-none-any.whl", hash = "sha256:bc3ddf669d234f4220e6e1c4d96b061abe0998185a8d7855c0126782b7abc8c1"},
+    {file = "pydantic-2.4.2.tar.gz", hash = "sha256:94f336138093a5d7f426aac732dcfe7ab4eb4da243c88f891d65deb4a2556ee7"},
 ]
 
 [package.dependencies]
 annotated-types = ">=0.4.0"
-pydantic-core = "2.14.1"
+pydantic-core = "2.10.1"
 typing-extensions = ">=4.6.1"
 
 [package.extras]
@@ -1475,112 +1415,117 @@ email = ["email-validator (>=2.0.0)"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.14.1"
+version = "2.10.1"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pydantic_core-2.14.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:812beca1dcb2b722cccc7e9c620bd972cbc323321194ec2725eab3222e6ac573"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a2ccdc53cb88e51c7d47d74c59630d7be844428f6b8d463055ffad6f0392d8da"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd937733bf2fe7d6a8bf208c12741f1f730b7bf5636033877767a75093c29b8a"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:581bb606a31749a00796f5257947a0968182d7fe91e1dada41f06aeb6bfbc91a"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aadf74a40a7ae49c3c1aa7d32334fe94f4f968e21dd948e301bb4ed431fb2412"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b89821a2c77cc1b8f2c1fc3aacd6a3ecc5df8f7e518dc3f18aef8c4dcf66003d"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49ee28d65f506b2858a60745cc974ed005298ebab12693646b97641dd7c99c35"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:97246f896b4df7fd84caa8a75a67abb95f94bc0b547665bf0889e3262b060399"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:1185548665bc61bbab0dc78f10c8eafa0db0aa1e920fe9a451b77782b10a65cc"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2a7d08b39fac97540fba785fce3b21ee01a81f081a07a4d031efd791da6666f9"},
-    {file = "pydantic_core-2.14.1-cp310-none-win32.whl", hash = "sha256:0a8c8daf4e3aa3aeb98e3638fc3d58a359738f3d12590b2474c6bb64031a0764"},
-    {file = "pydantic_core-2.14.1-cp310-none-win_amd64.whl", hash = "sha256:4f0788699a92d604f348e9c1ac5e97e304e97127ba8325c7d0af88dcc7d35bd3"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:2be018a84995b6be1bbd40d6064395dbf71592a981169cf154c0885637f5f54a"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fc3227408808ba7df8e95eb1d8389f4ba2203bed8240b308de1d7ae66d828f24"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42d5d0e9bbb50481a049bd0203224b339d4db04006b78564df2b782e2fd16ebc"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bc6a4ea9f88a810cb65ccae14404da846e2a02dd5c0ad21dee712ff69d142638"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d312ad20e3c6d179cb97c42232b53111bcd8dcdd5c1136083db9d6bdd489bc73"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:679cc4e184f213c8227862e57340d12fd4d4d19dc0e3ddb0f653f86f01e90f94"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:101df420e954966868b8bc992aefed5fa71dd1f2755104da62ee247abab28e2f"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c964c0cc443d6c08a2347c0e5c1fc2d85a272dc66c1a6f3cde4fc4843882ada4"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:8276bbab68a9dbe721da92d19cbc061f76655248fe24fb63969d0c3e0e5755e7"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:12163197fec7c95751a3c71b36dcc1909eed9959f011ffc79cc8170a6a74c826"},
-    {file = "pydantic_core-2.14.1-cp311-none-win32.whl", hash = "sha256:b8ff0302518dcd001bd722bbe342919c29e5066c7eda86828fe08cdc112668b8"},
-    {file = "pydantic_core-2.14.1-cp311-none-win_amd64.whl", hash = "sha256:59fa83873223f856d898452c6162a390af4297756f6ba38493a67533387d85d9"},
-    {file = "pydantic_core-2.14.1-cp311-none-win_arm64.whl", hash = "sha256:798590d38c9381f07c48d13af1f1ef337cebf76ee452fcec5deb04aceced51c7"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:587d75aec9ae50d0d63788cec38bf13c5128b3fc1411aa4b9398ebac884ab179"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:26242e3593d4929123615bd9365dd86ef79b7b0592d64a96cd11fd83c69c9f34"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5879ac4791508d8f0eb7dec71ff8521855180688dac0c55f8c99fc4d1a939845"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ad9ea86f5fc50f1b62c31184767fe0cacaa13b54fe57d38898c3776d30602411"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:102ac85a775e77821943ae38da9634ddd774b37a8d407181b4f7b05cdfb36b55"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2459cc06572730e079ec1e694e8f68c99d977b40d98748ae72ff11ef21a56b0b"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:217dcbfaf429a9b8f1d54eb380908b9c778e78f31378283b30ba463c21e89d5d"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9d59e0d7cdfe8ed1d4fcd28aad09625c715dc18976c7067e37d8a11b06f4be3e"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e2be646a5155d408e68b560c0553e8a83dc7b9f90ec6e5a2fc3ff216719385db"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ffba979801e3931a19cd30ed2049450820effe8f152aaa317e2fd93795d318d7"},
-    {file = "pydantic_core-2.14.1-cp312-none-win32.whl", hash = "sha256:132b40e479cb5cebbbb681f77aaceabbc8355df16c9124cff1d4060ada83cde2"},
-    {file = "pydantic_core-2.14.1-cp312-none-win_amd64.whl", hash = "sha256:744b807fe2733b6da3b53e8ad93e8b3ea3ee3dfc3abece4dd2824cc1f39aa343"},
-    {file = "pydantic_core-2.14.1-cp312-none-win_arm64.whl", hash = "sha256:24ba48f9d0b8d64fc5e42e1600366c3d7db701201294989aebdaca23110c02ab"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:ba55d73a2df4771b211d0bcdea8b79454980a81ed34a1d77a19ddcc81f98c895"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:e905014815687d88cbb14bbc0496420526cf20d49f20606537d87646b70f1046"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:443dc5eede7fa76b2370213e0abe881eb17c96f7d694501853c11d5d56916602"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:abae6fd5504e5e438e4f6f739f8364fd9ff5a5cdca897e68363e2318af90bc28"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9486e27bb3f137f33e2315be2baa0b0b983dae9e2f5f5395240178ad8e644728"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:69df82892ff00491d673b1929538efb8c8d68f534fdc6cb7fd3ac8a5852b9034"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:184ff7b30c3f60e1b775378c060099285fd4b5249271046c9005f8b247b39377"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3d5b2a4b3c10cad0615670cab99059441ff42e92cf793a0336f4bc611e895204"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:871c641a83719caaa856a11dcc61c5e5b35b0db888e1a0d338fe67ce744575e2"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:1e7208946ea9b27a8cef13822c339d4ae96e45952cc01fc4a91c7f1cb0ae2861"},
-    {file = "pydantic_core-2.14.1-cp37-none-win32.whl", hash = "sha256:b4ff385a525017f5adf6066d7f9fb309f99ade725dcf17ed623dc7dce1f85d9f"},
-    {file = "pydantic_core-2.14.1-cp37-none-win_amd64.whl", hash = "sha256:c7411cd06afeb263182e38c6ca5b4f5fe4f20d91466ad7db0cd6af453a02edec"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:2871daf5b2823bf77bf7d3d43825e5d904030c155affdf84b21a00a2e00821d2"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7977e261cac5f99873dc2c6f044315d09b19a71c4246560e1e67593889a90978"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5a111f9158555582deadd202a60bd7803b6c68f406391b7cf6905adf0af6811"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ac417312bf6b7a0223ba73fb12e26b2854c93bf5b1911f7afef6d24c379b22aa"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c36987f5eb2a7856b5f5feacc3be206b4d1852a6ce799f6799dd9ffb0cba56ae"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6e98227eb02623d57e1fd061788837834b68bb995a869565211b9abf3de4bf4"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:023b6d7ec4e97890b28eb2ee24413e69a6d48de4e8b75123957edd5432f4eeb3"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6015beb28deb5306049ecf2519a59627e9e050892927850a884df6d5672f8c7d"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:3f48d4afd973abbd65266ac24b24de1591116880efc7729caf6b6b94a9654c9e"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:28734bcfb8fc5b03293dec5eb5ea73b32ff767f6ef79a31f6e41dad2f5470270"},
-    {file = "pydantic_core-2.14.1-cp38-none-win32.whl", hash = "sha256:3303113fdfaca927ef11e0c5f109e2ec196c404f9d7ba5f8ddb63cdf287ea159"},
-    {file = "pydantic_core-2.14.1-cp38-none-win_amd64.whl", hash = "sha256:144f2c1d5579108b6ed1193fcc9926124bd4142b0f7020a7744980d1235c8a40"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:893bf4fb9bfb9c4639bc12f3de323325ada4c6d60e478d5cded65453e9364890"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:052d8731aaf844f91fe4cd3faf28983b109a5865b3a256ec550b80a5689ead87"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb1c6ecb53e4b907ee8486f453dd940b8cbb509946e2b671e3bf807d310a96fc"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:94cf6d0274eb899d39189144dcf52814c67f9b0fd196f211420d9aac793df2da"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:36c3bf96f803e207a80dbcb633d82b98ff02a9faa76dd446e969424dec8e2b9f"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fb290491f1f0786a7da4585250f1feee200fc17ff64855bdd7c42fb54526fa29"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6590ed9d13eb51b28ea17ddcc6c8dbd6050b4eb589d497105f0e13339f223b72"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:69cd74e55a5326d920e7b46daa2d81c2bdb8bcf588eafb2330d981297b742ddc"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d965bdb50725a805b083f5f58d05669a85705f50a6a864e31b545c589290ee31"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ca942a2dc066ca5e04c27feaa8dfb9d353ddad14c6641660c565149186095343"},
-    {file = "pydantic_core-2.14.1-cp39-none-win32.whl", hash = "sha256:72c2ef3787c3b577e5d6225d73a77167b942d12cef3c1fbd5e74e55b7f881c36"},
-    {file = "pydantic_core-2.14.1-cp39-none-win_amd64.whl", hash = "sha256:55713d155da1e508083c4b08d0b1ad2c3054f68b8ef7eb3d3864822e456f0bb5"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:53efe03cc383a83660cfdda6a3cb40ee31372cedea0fde0b2a2e55e838873ab6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:f523e116879bc6714e61d447ce934676473b068069dce6563ea040381dc7a257"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85bb66d661be51b2cba9ca06759264b3469d2dbb53c3e6effb3f05fec6322be6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f53a3ccdc30234cb4342cec541e3e6ed87799c7ca552f0b5f44e3967a5fed526"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:1bfb63821ada76719ffcd703fc40dd57962e0d8c253e3c565252e6de6d3e0bc6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e2c689439f262c29cf3fcd5364da1e64d8600facecf9eabea8643b8755d2f0de"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a15f6e5588f7afb7f6fc4b0f4ff064749e515d34f34c666ed6e37933873d8ad8"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:f1a30eef060e21af22c7d23349f1028de0611f522941c80efa51c05a63142c62"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16f4a7e1ec6b3ea98a1e108a2739710cd659d68b33fbbeaba066202cab69c7b6"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fd80a2d383940eec3db6a5b59d1820f947317acc5c75482ff8d79bf700f8ad6a"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:a68a36d71c7f638dda6c9e6b67f6aabf3fa1471b198d246457bfdc7c777cdeb7"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ebc79120e105e4bcd7865f369e3b9dbabb0d492d221e1a7f62a3e8e292550278"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:c8c466facec2ccdf025b0b1455b18f2c3d574d5f64d24df905d3d7b8f05d5f4e"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:b91b5ec423e88caa16777094c4b2b97f11453283e7a837e5e5e1b886abba1251"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:130e49aa0cb316f743bc7792c36aefa39fc2221312f1d4b333b19edbdd71f2b1"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f483467c046f549572f8aca3b7128829e09ae3a9fe933ea421f7cb7c58120edb"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:dee4682bd7947afc682d342a8d65ad1834583132383f8e801601a8698cb8d17a"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:8d927d042c0ef04607ee7822828b208ab045867d20477ec6593d612156798547"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5a1570875eb0d1479fb2270ed80c88c231aaaf68b0c3f114f35e7fb610435e4f"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:cb2fd3ab67558eb16aecfb4f2db4febb4d37dc74e6b8613dc2e7160fb58158a9"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a7991f25b98038252363a03e6a9fe92e60fe390fda2631d238dc3b0e396632f8"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5b45b7be9f99991405ecd6f6172fb6798908a8097106ae78d5cc5cc15121bad9"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:51506e7652a2ef1d1cf763c4b51b972ff4568d1dddc96ca83931a6941f5e6389"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:66dc0e63349ec39c1ea66622aa5c2c1f84382112afd3ab2fa0cca4fb01f7db39"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:8e17f0c3ba4cb07faa0038a59ce162de584ed48ba645c8d05a5de1e40d4c21e7"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d983222223f63e323a5f497f5b85e211557a5d8fb670dc88f343784502b466ba"},
-    {file = "pydantic_core-2.14.1.tar.gz", hash = "sha256:0d82a6ee815388a362885186e431fac84c7a06623bc136f508e9f88261d8cadb"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:d64728ee14e667ba27c66314b7d880b8eeb050e58ffc5fec3b7a109f8cddbd63"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:48525933fea744a3e7464c19bfede85df4aba79ce90c60b94d8b6e1eddd67096"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ef337945bbd76cce390d1b2496ccf9f90b1c1242a3a7bc242ca4a9fc5993427a"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a1392e0638af203cee360495fd2cfdd6054711f2db5175b6e9c3c461b76f5175"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0675ba5d22de54d07bccde38997e780044dcfa9a71aac9fd7d4d7a1d2e3e65f7"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:128552af70a64660f21cb0eb4876cbdadf1a1f9d5de820fed6421fa8de07c893"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f6e6aed5818c264412ac0598b581a002a9f050cb2637a84979859e70197aa9e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ecaac27da855b8d73f92123e5f03612b04c5632fd0a476e469dfc47cd37d6b2e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b3c01c2fb081fced3bbb3da78510693dc7121bb893a1f0f5f4b48013201f362e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:92f675fefa977625105708492850bcbc1182bfc3e997f8eecb866d1927c98ae6"},
+    {file = "pydantic_core-2.10.1-cp310-none-win32.whl", hash = "sha256:420a692b547736a8d8703c39ea935ab5d8f0d2573f8f123b0a294e49a73f214b"},
+    {file = "pydantic_core-2.10.1-cp310-none-win_amd64.whl", hash = "sha256:0880e239827b4b5b3e2ce05e6b766a7414e5f5aedc4523be6b68cfbc7f61c5d0"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:073d4a470b195d2b2245d0343569aac7e979d3a0dcce6c7d2af6d8a920ad0bea"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:600d04a7b342363058b9190d4e929a8e2e715c5682a70cc37d5ded1e0dd370b4"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39215d809470f4c8d1881758575b2abfb80174a9e8daf8f33b1d4379357e417c"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eeb3d3d6b399ffe55f9a04e09e635554012f1980696d6b0aca3e6cf42a17a03b"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a7a7902bf75779bc12ccfc508bfb7a4c47063f748ea3de87135d433a4cca7a2f"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3625578b6010c65964d177626fde80cf60d7f2e297d56b925cb5cdeda6e9925a"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:caa48fc31fc7243e50188197b5f0c4228956f97b954f76da157aae7f67269ae8"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:07ec6d7d929ae9c68f716195ce15e745b3e8fa122fc67698ac6498d802ed0fa4"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e6f31a17acede6a8cd1ae2d123ce04d8cca74056c9d456075f4f6f85de055607"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d8f1ebca515a03e5654f88411420fea6380fc841d1bea08effb28184e3d4899f"},
+    {file = "pydantic_core-2.10.1-cp311-none-win32.whl", hash = "sha256:6db2eb9654a85ada248afa5a6db5ff1cf0f7b16043a6b070adc4a5be68c716d6"},
+    {file = "pydantic_core-2.10.1-cp311-none-win_amd64.whl", hash = "sha256:4a5be350f922430997f240d25f8219f93b0c81e15f7b30b868b2fddfc2d05f27"},
+    {file = "pydantic_core-2.10.1-cp311-none-win_arm64.whl", hash = "sha256:5fdb39f67c779b183b0c853cd6b45f7db84b84e0571b3ef1c89cdb1dfc367325"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:b1f22a9ab44de5f082216270552aa54259db20189e68fc12484873d926426921"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8572cadbf4cfa95fb4187775b5ade2eaa93511f07947b38f4cd67cf10783b118"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:db9a28c063c7c00844ae42a80203eb6d2d6bbb97070cfa00194dff40e6f545ab"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0e2a35baa428181cb2270a15864ec6286822d3576f2ed0f4cd7f0c1708472aff"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:05560ab976012bf40f25d5225a58bfa649bb897b87192a36c6fef1ab132540d7"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d6495008733c7521a89422d7a68efa0a0122c99a5861f06020ef5b1f51f9ba7c"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14ac492c686defc8e6133e3a2d9eaf5261b3df26b8ae97450c1647286750b901"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8282bab177a9a3081fd3d0a0175a07a1e2bfb7fcbbd949519ea0980f8a07144d"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:aafdb89fdeb5fe165043896817eccd6434aee124d5ee9b354f92cd574ba5e78f"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:f6defd966ca3b187ec6c366604e9296f585021d922e666b99c47e78738b5666c"},
+    {file = "pydantic_core-2.10.1-cp312-none-win32.whl", hash = "sha256:7c4d1894fe112b0864c1fa75dffa045720a194b227bed12f4be7f6045b25209f"},
+    {file = "pydantic_core-2.10.1-cp312-none-win_amd64.whl", hash = "sha256:5994985da903d0b8a08e4935c46ed8daf5be1cf217489e673910951dc533d430"},
+    {file = "pydantic_core-2.10.1-cp312-none-win_arm64.whl", hash = "sha256:0d8a8adef23d86d8eceed3e32e9cca8879c7481c183f84ed1a8edc7df073af94"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:9badf8d45171d92387410b04639d73811b785b5161ecadabf056ea14d62d4ede"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:ebedb45b9feb7258fac0a268a3f6bec0a2ea4d9558f3d6f813f02ff3a6dc6698"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cfe1090245c078720d250d19cb05d67e21a9cd7c257698ef139bc41cf6c27b4f"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e357571bb0efd65fd55f18db0a2fb0ed89d0bb1d41d906b138f088933ae618bb"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b3dcd587b69bbf54fc04ca157c2323b8911033e827fffaecf0cafa5a892a0904"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c120c9ce3b163b985a3b966bb701114beb1da4b0468b9b236fc754783d85aa3"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15d6bca84ffc966cc9976b09a18cf9543ed4d4ecbd97e7086f9ce9327ea48891"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5cabb9710f09d5d2e9e2748c3e3e20d991a4c5f96ed8f1132518f54ab2967221"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:82f55187a5bebae7d81d35b1e9aaea5e169d44819789837cdd4720d768c55d15"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:1d40f55222b233e98e3921df7811c27567f0e1a4411b93d4c5c0f4ce131bc42f"},
+    {file = "pydantic_core-2.10.1-cp37-none-win32.whl", hash = "sha256:14e09ff0b8fe6e46b93d36a878f6e4a3a98ba5303c76bb8e716f4878a3bee92c"},
+    {file = "pydantic_core-2.10.1-cp37-none-win_amd64.whl", hash = "sha256:1396e81b83516b9d5c9e26a924fa69164156c148c717131f54f586485ac3c15e"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:6835451b57c1b467b95ffb03a38bb75b52fb4dc2762bb1d9dbed8de31ea7d0fc"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b00bc4619f60c853556b35f83731bd817f989cba3e97dc792bb8c97941b8053a"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0fa467fd300a6f046bdb248d40cd015b21b7576c168a6bb20aa22e595c8ffcdd"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d99277877daf2efe074eae6338453a4ed54a2d93fb4678ddfe1209a0c93a2468"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fa7db7558607afeccb33c0e4bf1c9a9a835e26599e76af6fe2fcea45904083a6"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:aad7bd686363d1ce4ee930ad39f14e1673248373f4a9d74d2b9554f06199fb58"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:443fed67d33aa85357464f297e3d26e570267d1af6fef1c21ca50921d2976302"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:042462d8d6ba707fd3ce9649e7bf268633a41018d6a998fb5fbacb7e928a183e"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ecdbde46235f3d560b18be0cb706c8e8ad1b965e5c13bbba7450c86064e96561"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ed550ed05540c03f0e69e6d74ad58d026de61b9eaebebbaaf8873e585cbb18de"},
+    {file = "pydantic_core-2.10.1-cp38-none-win32.whl", hash = "sha256:8cdbbd92154db2fec4ec973d45c565e767ddc20aa6dbaf50142676484cbff8ee"},
+    {file = "pydantic_core-2.10.1-cp38-none-win_amd64.whl", hash = "sha256:9f6f3e2598604956480f6c8aa24a3384dbf6509fe995d97f6ca6103bb8c2534e"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:655f8f4c8d6a5963c9a0687793da37b9b681d9ad06f29438a3b2326d4e6b7970"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e570ffeb2170e116a5b17e83f19911020ac79d19c96f320cbfa1fa96b470185b"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:64322bfa13e44c6c30c518729ef08fda6026b96d5c0be724b3c4ae4da939f875"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:485a91abe3a07c3a8d1e082ba29254eea3e2bb13cbbd4351ea4e5a21912cc9b0"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f7c2b8eb9fc872e68b46eeaf835e86bccc3a58ba57d0eedc109cbb14177be531"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a5cb87bdc2e5f620693148b5f8f842d293cae46c5f15a1b1bf7ceeed324a740c"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:25bd966103890ccfa028841a8f30cebcf5875eeac8c4bde4fe221364c92f0c9a"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f323306d0556351735b54acbf82904fe30a27b6a7147153cbe6e19aaaa2aa429"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0c27f38dc4fbf07b358b2bc90edf35e82d1703e22ff2efa4af4ad5de1b3833e7"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:f1365e032a477c1430cfe0cf2856679529a2331426f8081172c4a74186f1d595"},
+    {file = "pydantic_core-2.10.1-cp39-none-win32.whl", hash = "sha256:a1c311fd06ab3b10805abb72109f01a134019739bd3286b8ae1bc2fc4e50c07a"},
+    {file = "pydantic_core-2.10.1-cp39-none-win_amd64.whl", hash = "sha256:ae8a8843b11dc0b03b57b52793e391f0122e740de3df1474814c700d2622950a"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:d43002441932f9a9ea5d6f9efaa2e21458221a3a4b417a14027a1d530201ef1b"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:fcb83175cc4936a5425dde3356f079ae03c0802bbdf8ff82c035f8a54b333521"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:962ed72424bf1f72334e2f1e61b68f16c0e596f024ca7ac5daf229f7c26e4208"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2cf5bb4dd67f20f3bbc1209ef572a259027c49e5ff694fa56bed62959b41e1f9"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e544246b859f17373bed915182ab841b80849ed9cf23f1f07b73b7c58baee5fb"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:c0877239307b7e69d025b73774e88e86ce82f6ba6adf98f41069d5b0b78bd1bf"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:53df009d1e1ba40f696f8995683e067e3967101d4bb4ea6f667931b7d4a01357"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a1254357f7e4c82e77c348dabf2d55f1d14d19d91ff025004775e70a6ef40ada"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:524ff0ca3baea164d6d93a32c58ac79eca9f6cf713586fdc0adb66a8cdeab96a"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f0ac9fb8608dbc6eaf17956bf623c9119b4db7dbb511650910a82e261e6600f"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:320f14bd4542a04ab23747ff2c8a778bde727158b606e2661349557f0770711e"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:63974d168b6233b4ed6a0046296803cb13c56637a7b8106564ab575926572a55"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:417243bf599ba1f1fef2bb8c543ceb918676954734e2dcb82bf162ae9d7bd514"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:dda81e5ec82485155a19d9624cfcca9be88a405e2857354e5b089c2a982144b2"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:14cfbb00959259e15d684505263d5a21732b31248a5dd4941f73a3be233865b9"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:631cb7415225954fdcc2a024119101946793e5923f6c4d73a5914d27eb3d3a05"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:bec7dd208a4182e99c5b6c501ce0b1f49de2802448d4056091f8e630b28e9a52"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:149b8a07712f45b332faee1a2258d8ef1fb4a36f88c0c17cb687f205c5dc6e7d"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4d966c47f9dd73c2d32a809d2be529112d509321c5310ebf54076812e6ecd884"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7eb037106f5c6b3b0b864ad226b0b7ab58157124161d48e4b30c4a43fef8bc4b"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:154ea7c52e32dce13065dbb20a4a6f0cc012b4f667ac90d648d36b12007fa9f7"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e562617a45b5a9da5be4abe72b971d4f00bf8555eb29bb91ec2ef2be348cd132"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:f23b55eb5464468f9e0e9a9935ce3ed2a870608d5f534025cd5536bca25b1402"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:e9121b4009339b0f751955baf4543a0bfd6bc3f8188f8056b1a25a2d45099934"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:0523aeb76e03f753b58be33b26540880bac5aa54422e4462404c432230543f33"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e0e2959ef5d5b8dc9ef21e1a305a21a36e254e6a34432d00c72a92fdc5ecda5"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da01bec0a26befab4898ed83b362993c844b9a607a86add78604186297eb047e"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f2e9072d71c1f6cfc79a36d4484c82823c560e6f5599c43c1ca6b5cdbd54f881"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:f36a3489d9e28fe4b67be9992a23029c3cec0babc3bd9afb39f49844a8c721c5"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f64f82cc3443149292b32387086d02a6c7fb39b8781563e0ca7b8d7d9cf72bd7"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b4a6db486ac8e99ae696e09efc8b2b9fea67b63c8f88ba7a1a16c24a057a0776"},
+    {file = "pydantic_core-2.10.1.tar.gz", hash = "sha256:0f8682dbdd2f67f8e1edddcbffcc29f60a6182b4901c367fc8c1c40d30bb0a82"},
 ]
 
 [package.dependencies]
@@ -2101,60 +2046,12 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.23"
+version = "2.0.22"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:638c2c0b6b4661a4fd264f6fb804eccd392745c5887f9317feb64bb7cb03b3ea"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e3b5036aa326dc2df50cba3c958e29b291a80f604b1afa4c8ce73e78e1c9f01d"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:787af80107fb691934a01889ca8f82a44adedbf5ef3d6ad7d0f0b9ac557e0c34"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c14eba45983d2f48f7546bb32b47937ee2cafae353646295f0e99f35b14286ab"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0666031df46b9badba9bed00092a1ffa3aa063a5e68fa244acd9f08070e936d3"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:89a01238fcb9a8af118eaad3ffcc5dedaacbd429dc6fdc43fe430d3a941ff965"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-win32.whl", hash = "sha256:cabafc7837b6cec61c0e1e5c6d14ef250b675fa9c3060ed8a7e38653bd732ff8"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-win_amd64.whl", hash = "sha256:87a3d6b53c39cd173990de2f5f4b83431d534a74f0e2f88bd16eabb5667e65c6"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d5578e6863eeb998980c212a39106ea139bdc0b3f73291b96e27c929c90cd8e1"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:62d9e964870ea5ade4bc870ac4004c456efe75fb50404c03c5fd61f8bc669a72"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c80c38bd2ea35b97cbf7c21aeb129dcbebbf344ee01a7141016ab7b851464f8e"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75eefe09e98043cff2fb8af9796e20747ae870c903dc61d41b0c2e55128f958d"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:bd45a5b6c68357578263d74daab6ff9439517f87da63442d244f9f23df56138d"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:a86cb7063e2c9fb8e774f77fbf8475516d270a3e989da55fa05d08089d77f8c4"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-win32.whl", hash = "sha256:b41f5d65b54cdf4934ecede2f41b9c60c9f785620416e8e6c48349ab18643855"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-win_amd64.whl", hash = "sha256:9ca922f305d67605668e93991aaf2c12239c78207bca3b891cd51a4515c72e22"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d0f7fb0c7527c41fa6fcae2be537ac137f636a41b4c5a4c58914541e2f436b45"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7c424983ab447dab126c39d3ce3be5bee95700783204a72549c3dceffe0fc8f4"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f508ba8f89e0a5ecdfd3761f82dda2a3d7b678a626967608f4273e0dba8f07ac"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6463aa765cf02b9247e38b35853923edbf2f6fd1963df88706bc1d02410a5577"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e599a51acf3cc4d31d1a0cf248d8f8d863b6386d2b6782c5074427ebb7803bda"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fd54601ef9cc455a0c61e5245f690c8a3ad67ddb03d3b91c361d076def0b4c60"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-win32.whl", hash = "sha256:42d0b0290a8fb0165ea2c2781ae66e95cca6e27a2fbe1016ff8db3112ac1e846"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-win_amd64.whl", hash = "sha256:227135ef1e48165f37590b8bfc44ed7ff4c074bf04dc8d6f8e7f1c14a94aa6ca"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:14aebfe28b99f24f8a4c1346c48bc3d63705b1f919a24c27471136d2f219f02d"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e983fa42164577d073778d06d2cc5d020322425a509a08119bdcee70ad856bf"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e0dc9031baa46ad0dd5a269cb7a92a73284d1309228be1d5935dac8fb3cae24"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5f94aeb99f43729960638e7468d4688f6efccb837a858b34574e01143cf11f89"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:63bfc3acc970776036f6d1d0e65faa7473be9f3135d37a463c5eba5efcdb24c8"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-win32.whl", hash = "sha256:f48ed89dd11c3c586f45e9eec1e437b355b3b6f6884ea4a4c3111a3358fd0c18"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-win_amd64.whl", hash = "sha256:1e018aba8363adb0599e745af245306cb8c46b9ad0a6fc0a86745b6ff7d940fc"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:64ac935a90bc479fee77f9463f298943b0e60005fe5de2aa654d9cdef46c54df"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c4722f3bc3c1c2fcc3702dbe0016ba31148dd6efcd2a2fd33c1b4897c6a19693"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4af79c06825e2836de21439cb2a6ce22b2ca129bad74f359bddd173f39582bf5"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:683ef58ca8eea4747737a1c35c11372ffeb84578d3aab8f3e10b1d13d66f2bc4"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:d4041ad05b35f1f4da481f6b811b4af2f29e83af253bf37c3c4582b2c68934ab"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:aeb397de65a0a62f14c257f36a726945a7f7bb60253462e8602d9b97b5cbe204"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-win32.whl", hash = "sha256:42ede90148b73fe4ab4a089f3126b2cfae8cfefc955c8174d697bb46210c8306"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-win_amd64.whl", hash = "sha256:964971b52daab357d2c0875825e36584d58f536e920f2968df8d581054eada4b"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:616fe7bcff0a05098f64b4478b78ec2dfa03225c23734d83d6c169eb41a93e55"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0e680527245895aba86afbd5bef6c316831c02aa988d1aad83c47ffe92655e74"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9585b646ffb048c0250acc7dad92536591ffe35dba624bb8fd9b471e25212a35"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4895a63e2c271ffc7a81ea424b94060f7b3b03b4ea0cd58ab5bb676ed02f4221"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:cc1d21576f958c42d9aec68eba5c1a7d715e5fc07825a629015fe8e3b0657fb0"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:967c0b71156f793e6662dd839da54f884631755275ed71f1539c95bbada9aaab"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-win32.whl", hash = "sha256:0a8c6aa506893e25a04233bc721c6b6cf844bafd7250535abb56cb6cc1368884"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-win_amd64.whl", hash = "sha256:f3420d00d2cb42432c1d0e44540ae83185ccbbc67a6054dcc8ab5387add6620b"},
-    {file = "SQLAlchemy-2.0.23-py3-none-any.whl", hash = "sha256:31952bbc527d633b9479f5f81e8b9dfada00b91d6baba021a869095f1a97006d"},
-    {file = "SQLAlchemy-2.0.23.tar.gz", hash = "sha256:c1bda93cbbe4aa2aa0aa8655c5aeda505cd219ff3e8da91d1d329e143e4aff69"},
+    {file = "SQLAlchemy-2.0.22.tar.gz", hash = "sha256:5434cc601aa17570d79e5377f5fd45ff92f9379e2abed0be5e8c2fba8d353d2b"},
 ]
 
 [package.dependencies]
@@ -2163,8 +2060,7 @@ typing-extensions = ">=4.2.0"
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
-aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@@ -2174,7 +2070,7 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)"]
 mysql = ["mysqlclient (>=1.4.0)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx-oracle (>=8)"]
+oracle = ["cx_oracle (>=7)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
@@ -2184,7 +2080,7 @@ postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
 postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "sse-starlette"
@@ -2528,13 +2424,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.35.1"
+version = "4.34.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.35.1-py3-none-any.whl", hash = "sha256:5e70b103b96e639fb40d4c4e833a45f857ffa60d6bd8ef486721d1ee7542e148"},
-    {file = "transformers-4.35.1.tar.gz", hash = "sha256:81d4f029cdbdf51b872e28b74167759b3703ef7f81289c0bf8f5085f940437e7"},
+    {file = "transformers-4.34.1-py3-none-any.whl", hash = "sha256:d06ac09151d7b845e4a4acd6b143a591d946031ee67b4cbb20693b241920ffc0"},
+    {file = "transformers-4.34.1.tar.gz", hash = "sha256:1d0258d5a18063b66005bbe1e3276ec5943d9ab4ab47f020db1fd485cc40ea22"},
 ]
 
 [package.dependencies]
@@ -2556,12 +2452,13 @@ all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.15)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.15)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
 docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -2581,7 +2478,7 @@ serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -2646,17 +2543,18 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "urllib3"
-version = "2.1.0"
+version = "2.0.7"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+    {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
+    {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
@@ -2769,4 +2667,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "6bf535039896ca5408080bd2a4b3dd13a6335152ed07aca78166d8bfb879a7c2"
+content-hash = "345aa46bf4a24e70ced213602f49ca03f750d9b717e905e1d22106294cbeaa06"

templates/rag-self-query/pyproject.toml

@@ -7,7 +7,7 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain = ">=0.0.335"
+langchain = ">=0.0.325"
 openai = "^0.28.1"
 elasticsearch = "^8.10.0"
 sentence-transformers = "^2.2.2"

templates/rag-timescale-conversation/poetry.lock

@@ -274,101 +274,101 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-win32.whl", hash = "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d"},
-    {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
+    {file = "charset-normalizer-3.3.0.tar.gz", hash = "sha256:63563193aec44bce707e0c5ca64ff69fa72ed7cf34ce6e11d5127555756fd2f6"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:effe5406c9bd748a871dbcaf3ac69167c38d72db8c9baf3ff954c344f31c4cbe"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4162918ef3098851fcd8a628bf9b6a98d10c380725df9e04caf5ca6dd48c847a"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0570d21da019941634a531444364f2482e8db0b3425fcd5ac0c36565a64142c8"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5707a746c6083a3a74b46b3a631d78d129edab06195a92a8ece755aac25a3f3d"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:278c296c6f96fa686d74eb449ea1697f3c03dc28b75f873b65b5201806346a69"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a4b71f4d1765639372a3b32d2638197f5cd5221b19531f9245fcc9ee62d38f56"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5969baeaea61c97efa706b9b107dcba02784b1601c74ac84f2a532ea079403e"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a3f93dab657839dfa61025056606600a11d0b696d79386f974e459a3fbc568ec"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:db756e48f9c5c607b5e33dd36b1d5872d0422e960145b08ab0ec7fd420e9d649"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:232ac332403e37e4a03d209a3f92ed9071f7d3dbda70e2a5e9cff1c4ba9f0678"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e5c1502d4ace69a179305abb3f0bb6141cbe4714bc9b31d427329a95acfc8bdd"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:2502dd2a736c879c0f0d3e2161e74d9907231e25d35794584b1ca5284e43f596"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:23e8565ab7ff33218530bc817922fae827420f143479b753104ab801145b1d5b"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-win32.whl", hash = "sha256:1872d01ac8c618a8da634e232f24793883d6e456a66593135aeafe3784b0848d"},
+    {file = "charset_normalizer-3.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:557b21a44ceac6c6b9773bc65aa1b4cc3e248a5ad2f5b914b91579a32e22204d"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:d7eff0f27edc5afa9e405f7165f85a6d782d308f3b6b9d96016c010597958e63"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a685067d05e46641d5d1623d7c7fdf15a357546cbb2f71b0ebde91b175ffc3e"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0d3d5b7db9ed8a2b11a774db2bbea7ba1884430a205dbd54a32d61d7c2a190fa"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2935ffc78db9645cb2086c2f8f4cfd23d9b73cc0dc80334bc30aac6f03f68f8c"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9fe359b2e3a7729010060fbca442ca225280c16e923b37db0e955ac2a2b72a05"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:380c4bde80bce25c6e4f77b19386f5ec9db230df9f2f2ac1e5ad7af2caa70459"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0d1e3732768fecb052d90d62b220af62ead5748ac51ef61e7b32c266cac9293"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1b2919306936ac6efb3aed1fbf81039f7087ddadb3160882a57ee2ff74fd2382"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f8888e31e3a85943743f8fc15e71536bda1c81d5aa36d014a3c0c44481d7db6e"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:82eb849f085624f6a607538ee7b83a6d8126df6d2f7d3b319cb837b289123078"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7b8b8bf1189b3ba9b8de5c8db4d541b406611a71a955bbbd7385bbc45fcb786c"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:5adf257bd58c1b8632046bbe43ee38c04e1038e9d37de9c57a94d6bd6ce5da34"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c350354efb159b8767a6244c166f66e67506e06c8924ed74669b2c70bc8735b1"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-win32.whl", hash = "sha256:02af06682e3590ab952599fbadac535ede5d60d78848e555aa58d0c0abbde786"},
+    {file = "charset_normalizer-3.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:86d1f65ac145e2c9ed71d8ffb1905e9bba3a91ae29ba55b4c46ae6fc31d7c0d4"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:3b447982ad46348c02cb90d230b75ac34e9886273df3a93eec0539308a6296d7"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:abf0d9f45ea5fb95051c8bfe43cb40cda383772f7e5023a83cc481ca2604d74e"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b09719a17a2301178fac4470d54b1680b18a5048b481cb8890e1ef820cb80455"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b3d9b48ee6e3967b7901c052b670c7dda6deb812c309439adaffdec55c6d7b78"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:edfe077ab09442d4ef3c52cb1f9dab89bff02f4524afc0acf2d46be17dc479f5"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3debd1150027933210c2fc321527c2299118aa929c2f5a0a80ab6953e3bd1908"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86f63face3a527284f7bb8a9d4f78988e3c06823f7bea2bd6f0e0e9298ca0403"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:24817cb02cbef7cd499f7c9a2735286b4782bd47a5b3516a0e84c50eab44b98e"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:c71f16da1ed8949774ef79f4a0260d28b83b3a50c6576f8f4f0288d109777989"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:9cf3126b85822c4e53aa28c7ec9869b924d6fcfb76e77a45c44b83d91afd74f9"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:b3b2316b25644b23b54a6f6401074cebcecd1244c0b8e80111c9a3f1c8e83d65"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:03680bb39035fbcffe828eae9c3f8afc0428c91d38e7d61aa992ef7a59fb120e"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4cc152c5dd831641e995764f9f0b6589519f6f5123258ccaca8c6d34572fefa8"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-win32.whl", hash = "sha256:b8f3307af845803fb0b060ab76cf6dd3a13adc15b6b451f54281d25911eb92df"},
+    {file = "charset_normalizer-3.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:8eaf82f0eccd1505cf39a45a6bd0a8cf1c70dcfc30dba338207a969d91b965c0"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:dc45229747b67ffc441b3de2f3ae5e62877a282ea828a5bdb67883c4ee4a8810"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f4a0033ce9a76e391542c182f0d48d084855b5fcba5010f707c8e8c34663d77"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ada214c6fa40f8d800e575de6b91a40d0548139e5dc457d2ebb61470abf50186"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b1121de0e9d6e6ca08289583d7491e7fcb18a439305b34a30b20d8215922d43c"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1063da2c85b95f2d1a430f1c33b55c9c17ffaf5e612e10aeaad641c55a9e2b9d"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:70f1d09c0d7748b73290b29219e854b3207aea922f839437870d8cc2168e31cc"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:250c9eb0f4600361dd80d46112213dff2286231d92d3e52af1e5a6083d10cad9"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:750b446b2ffce1739e8578576092179160f6d26bd5e23eb1789c4d64d5af7dc7"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:fc52b79d83a3fe3a360902d3f5d79073a993597d48114c29485e9431092905d8"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:588245972aca710b5b68802c8cad9edaa98589b1b42ad2b53accd6910dad3545"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:e39c7eb31e3f5b1f88caff88bcff1b7f8334975b46f6ac6e9fc725d829bc35d4"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-win32.whl", hash = "sha256:abecce40dfebbfa6abf8e324e1860092eeca6f7375c8c4e655a8afb61af58f2c"},
+    {file = "charset_normalizer-3.3.0-cp37-cp37m-win_amd64.whl", hash = "sha256:24a91a981f185721542a0b7c92e9054b7ab4fea0508a795846bc5b0abf8118d4"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:67b8cc9574bb518ec76dc8e705d4c39ae78bb96237cb533edac149352c1f39fe"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ac71b2977fb90c35d41c9453116e283fac47bb9096ad917b8819ca8b943abecd"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3ae38d325b512f63f8da31f826e6cb6c367336f95e418137286ba362925c877e"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:542da1178c1c6af8873e143910e2269add130a299c9106eef2594e15dae5e482"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:30a85aed0b864ac88309b7d94be09f6046c834ef60762a8833b660139cfbad13"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:aae32c93e0f64469f74ccc730a7cb21c7610af3a775157e50bbd38f816536b38"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15b26ddf78d57f1d143bdf32e820fd8935d36abe8a25eb9ec0b5a71c82eb3895"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7f5d10bae5d78e4551b7be7a9b29643a95aded9d0f602aa2ba584f0388e7a557"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:249c6470a2b60935bafd1d1d13cd613f8cd8388d53461c67397ee6a0f5dce741"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:c5a74c359b2d47d26cdbbc7845e9662d6b08a1e915eb015d044729e92e7050b7"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:b5bcf60a228acae568e9911f410f9d9e0d43197d030ae5799e20dca8df588287"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:187d18082694a29005ba2944c882344b6748d5be69e3a89bf3cc9d878e548d5a"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:81bf654678e575403736b85ba3a7867e31c2c30a69bc57fe88e3ace52fb17b89"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-win32.whl", hash = "sha256:85a32721ddde63c9df9ebb0d2045b9691d9750cb139c161c80e500d210f5e26e"},
+    {file = "charset_normalizer-3.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:468d2a840567b13a590e67dd276c570f8de00ed767ecc611994c301d0f8c014f"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e0fc42822278451bc13a2e8626cf2218ba570f27856b536e00cfa53099724828"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:09c77f964f351a7369cc343911e0df63e762e42bac24cd7d18525961c81754f4"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:12ebea541c44fdc88ccb794a13fe861cc5e35d64ed689513a5c03d05b53b7c82"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:805dfea4ca10411a5296bcc75638017215a93ffb584c9e344731eef0dcfb026a"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:96c2b49eb6a72c0e4991d62406e365d87067ca14c1a729a870d22354e6f68115"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:aaf7b34c5bc56b38c931a54f7952f1ff0ae77a2e82496583b247f7c969eb1479"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:619d1c96099be5823db34fe89e2582b336b5b074a7f47f819d6b3a57ff7bdb86"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a0ac5e7015a5920cfce654c06618ec40c33e12801711da6b4258af59a8eff00a"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:93aa7eef6ee71c629b51ef873991d6911b906d7312c6e8e99790c0f33c576f89"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7966951325782121e67c81299a031f4c115615e68046f79b85856b86ebffc4cd"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:02673e456dc5ab13659f85196c534dc596d4ef260e4d86e856c3b2773ce09843"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:c2af80fb58f0f24b3f3adcb9148e6203fa67dd3f61c4af146ecad033024dde43"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:153e7b6e724761741e0974fc4dcd406d35ba70b92bfe3fedcb497226c93b9da7"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-win32.whl", hash = "sha256:d47ecf253780c90ee181d4d871cd655a789da937454045b17b5798da9393901a"},
+    {file = "charset_normalizer-3.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:d97d85fa63f315a8bdaba2af9a6a686e0eceab77b3089af45133252618e70884"},
+    {file = "charset_normalizer-3.3.0-py3-none-any.whl", hash = "sha256:e46cd37076971c1040fc8c41273a8b3e2c624ce4f2be3f5dfcb7a430c1d3acc2"},
 ]
 
 [[package]]
@@ -398,30 +398,19 @@ files = [
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.2"
+version = "0.6.1"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "dataclasses_json-0.6.2-py3-none-any.whl", hash = "sha256:71816ced3d0f55a2c5bc1a813ace1b8d4234e79a08744269a7cf84d6f7c06e99"},
-    {file = "dataclasses_json-0.6.2.tar.gz", hash = "sha256:1b934c1bd63e775880946b8361a902d7de86e894bab8098eab27c010f95724d1"},
+    {file = "dataclasses_json-0.6.1-py3-none-any.whl", hash = "sha256:1bd8418a61fe3d588bb0079214d7fb71d44937da40742b787256fd53b26b6c80"},
+    {file = "dataclasses_json-0.6.1.tar.gz", hash = "sha256:a53c220c35134ce08211a1057fd0e5bf76dc5331627c6b241cacbc570a89faae"},
 ]
 
 [package.dependencies]
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
-[[package]]
-name = "distro"
-version = "1.8.0"
-description = "Distro - an OS platform information API"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "distro-1.8.0-py3-none-any.whl", hash = "sha256:99522ca3e365cac527b44bde033f64c6945d90eb9f769703caaec52b09bbd3ff"},
-    {file = "distro-1.8.0.tar.gz", hash = "sha256:02e111d1dc6a50abb8eed6bf31c3e48ed8b0830d1ea2a1b78c61765c2513fdd8"},
-]
-
 [[package]]
 name = "dnspython"
 version = "2.4.2"
@@ -578,68 +567,74 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre
 
 [[package]]
 name = "greenlet"
-version = "3.0.1"
+version = "3.0.0"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "greenlet-3.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f89e21afe925fcfa655965ca8ea10f24773a1791400989ff32f467badfe4a064"},
-    {file = "greenlet-3.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:28e89e232c7593d33cac35425b58950789962011cc274aa43ef8865f2e11f46d"},
-    {file = "greenlet-3.0.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b8ba29306c5de7717b5761b9ea74f9c72b9e2b834e24aa984da99cbfc70157fd"},
-    {file = "greenlet-3.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:19bbdf1cce0346ef7341705d71e2ecf6f41a35c311137f29b8a2dc2341374565"},
-    {file = "greenlet-3.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:599daf06ea59bfedbec564b1692b0166a0045f32b6f0933b0dd4df59a854caf2"},
-    {file = "greenlet-3.0.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b641161c302efbb860ae6b081f406839a8b7d5573f20a455539823802c655f63"},
-    {file = "greenlet-3.0.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d57e20ba591727da0c230ab2c3f200ac9d6d333860d85348816e1dca4cc4792e"},
-    {file = "greenlet-3.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:5805e71e5b570d490938d55552f5a9e10f477c19400c38bf1d5190d760691846"},
-    {file = "greenlet-3.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:52e93b28db27ae7d208748f45d2db8a7b6a380e0d703f099c949d0f0d80b70e9"},
-    {file = "greenlet-3.0.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f7bfb769f7efa0eefcd039dd19d843a4fbfbac52f1878b1da2ed5793ec9b1a65"},
-    {file = "greenlet-3.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:91e6c7db42638dc45cf2e13c73be16bf83179f7859b07cfc139518941320be96"},
-    {file = "greenlet-3.0.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1757936efea16e3f03db20efd0cd50a1c86b06734f9f7338a90c4ba85ec2ad5a"},
-    {file = "greenlet-3.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:19075157a10055759066854a973b3d1325d964d498a805bb68a1f9af4aaef8ec"},
-    {file = "greenlet-3.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e9d21aaa84557d64209af04ff48e0ad5e28c5cca67ce43444e939579d085da72"},
-    {file = "greenlet-3.0.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2847e5d7beedb8d614186962c3d774d40d3374d580d2cbdab7f184580a39d234"},
-    {file = "greenlet-3.0.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:97e7ac860d64e2dcba5c5944cfc8fa9ea185cd84061c623536154d5a89237884"},
-    {file = "greenlet-3.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b2c02d2ad98116e914d4f3155ffc905fd0c025d901ead3f6ed07385e19122c94"},
-    {file = "greenlet-3.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:22f79120a24aeeae2b4471c711dcf4f8c736a2bb2fabad2a67ac9a55ea72523c"},
-    {file = "greenlet-3.0.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:100f78a29707ca1525ea47388cec8a049405147719f47ebf3895e7509c6446aa"},
-    {file = "greenlet-3.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:60d5772e8195f4e9ebf74046a9121bbb90090f6550f81d8956a05387ba139353"},
-    {file = "greenlet-3.0.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:daa7197b43c707462f06d2c693ffdbb5991cbb8b80b5b984007de431493a319c"},
-    {file = "greenlet-3.0.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ea6b8aa9e08eea388c5f7a276fabb1d4b6b9d6e4ceb12cc477c3d352001768a9"},
-    {file = "greenlet-3.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d11ebbd679e927593978aa44c10fc2092bc454b7d13fdc958d3e9d508aba7d0"},
-    {file = "greenlet-3.0.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dbd4c177afb8a8d9ba348d925b0b67246147af806f0b104af4d24f144d461cd5"},
-    {file = "greenlet-3.0.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:20107edf7c2c3644c67c12205dc60b1bb11d26b2610b276f97d666110d1b511d"},
-    {file = "greenlet-3.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8bef097455dea90ffe855286926ae02d8faa335ed8e4067326257cb571fc1445"},
-    {file = "greenlet-3.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:b2d3337dcfaa99698aa2377c81c9ca72fcd89c07e7eb62ece3f23a3fe89b2ce4"},
-    {file = "greenlet-3.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:80ac992f25d10aaebe1ee15df45ca0d7571d0f70b645c08ec68733fb7a020206"},
-    {file = "greenlet-3.0.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:337322096d92808f76ad26061a8f5fccb22b0809bea39212cd6c406f6a7060d2"},
-    {file = "greenlet-3.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9934adbd0f6e476f0ecff3c94626529f344f57b38c9a541f87098710b18af0a"},
-    {file = "greenlet-3.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dc4d815b794fd8868c4d67602692c21bf5293a75e4b607bb92a11e821e2b859a"},
-    {file = "greenlet-3.0.1-cp37-cp37m-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:41bdeeb552d814bcd7fb52172b304898a35818107cc8778b5101423c9017b3de"},
-    {file = "greenlet-3.0.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:6e6061bf1e9565c29002e3c601cf68569c450be7fc3f7336671af7ddb4657166"},
-    {file = "greenlet-3.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:fa24255ae3c0ab67e613556375a4341af04a084bd58764731972bcbc8baeba36"},
-    {file = "greenlet-3.0.1-cp37-cp37m-win32.whl", hash = "sha256:b489c36d1327868d207002391f662a1d163bdc8daf10ab2e5f6e41b9b96de3b1"},
-    {file = "greenlet-3.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:f33f3258aae89da191c6ebaa3bc517c6c4cbc9b9f689e5d8452f7aedbb913fa8"},
-    {file = "greenlet-3.0.1-cp38-cp38-macosx_11_0_universal2.whl", hash = "sha256:d2905ce1df400360463c772b55d8e2518d0e488a87cdea13dd2c71dcb2a1fa16"},
-    {file = "greenlet-3.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a02d259510b3630f330c86557331a3b0e0c79dac3d166e449a39363beaae174"},
-    {file = "greenlet-3.0.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:55d62807f1c5a1682075c62436702aaba941daa316e9161e4b6ccebbbf38bda3"},
-    {file = "greenlet-3.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3fcc780ae8edbb1d050d920ab44790201f027d59fdbd21362340a85c79066a74"},
-    {file = "greenlet-3.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4eddd98afc726f8aee1948858aed9e6feeb1758889dfd869072d4465973f6bfd"},
-    {file = "greenlet-3.0.1-cp38-cp38-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eabe7090db68c981fca689299c2d116400b553f4b713266b130cfc9e2aa9c5a9"},
-    {file = "greenlet-3.0.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:f2f6d303f3dee132b322a14cd8765287b8f86cdc10d2cb6a6fae234ea488888e"},
-    {file = "greenlet-3.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d923ff276f1c1f9680d32832f8d6c040fe9306cbfb5d161b0911e9634be9ef0a"},
-    {file = "greenlet-3.0.1-cp38-cp38-win32.whl", hash = "sha256:0b6f9f8ca7093fd4433472fd99b5650f8a26dcd8ba410e14094c1e44cd3ceddd"},
-    {file = "greenlet-3.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:990066bff27c4fcf3b69382b86f4c99b3652bab2a7e685d968cd4d0cfc6f67c6"},
-    {file = "greenlet-3.0.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:ce85c43ae54845272f6f9cd8320d034d7a946e9773c693b27d620edec825e376"},
-    {file = "greenlet-3.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89ee2e967bd7ff85d84a2de09df10e021c9b38c7d91dead95b406ed6350c6997"},
-    {file = "greenlet-3.0.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:87c8ceb0cf8a5a51b8008b643844b7f4a8264a2c13fcbcd8a8316161725383fe"},
-    {file = "greenlet-3.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d6a8c9d4f8692917a3dc7eb25a6fb337bff86909febe2f793ec1928cd97bedfc"},
-    {file = "greenlet-3.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fbc5b8f3dfe24784cee8ce0be3da2d8a79e46a276593db6868382d9c50d97b1"},
-    {file = "greenlet-3.0.1-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:85d2b77e7c9382f004b41d9c72c85537fac834fb141b0296942d52bf03fe4a3d"},
-    {file = "greenlet-3.0.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:696d8e7d82398e810f2b3622b24e87906763b6ebfd90e361e88eb85b0e554dc8"},
-    {file = "greenlet-3.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:329c5a2e5a0ee942f2992c5e3ff40be03e75f745f48847f118a3cfece7a28546"},
-    {file = "greenlet-3.0.1-cp39-cp39-win32.whl", hash = "sha256:cf868e08690cb89360eebc73ba4be7fb461cfbc6168dd88e2fbbe6f31812cd57"},
-    {file = "greenlet-3.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:ac4a39d1abae48184d420aa8e5e63efd1b75c8444dd95daa3e03f6c6310e9619"},
-    {file = "greenlet-3.0.1.tar.gz", hash = "sha256:816bd9488a94cba78d93e1abb58000e8266fa9cc2aa9ccdd6eb0696acb24005b"},
+    {file = "greenlet-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e09dea87cc91aea5500262993cbd484b41edf8af74f976719dd83fe724644cd6"},
+    {file = "greenlet-3.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f47932c434a3c8d3c86d865443fadc1fbf574e9b11d6650b656e602b1797908a"},
+    {file = "greenlet-3.0.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bdfaeecf8cc705d35d8e6de324bf58427d7eafb55f67050d8f28053a3d57118c"},
+    {file = "greenlet-3.0.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6a68d670c8f89ff65c82b936275369e532772eebc027c3be68c6b87ad05ca695"},
+    {file = "greenlet-3.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38ad562a104cd41e9d4644f46ea37167b93190c6d5e4048fcc4b80d34ecb278f"},
+    {file = "greenlet-3.0.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02a807b2a58d5cdebb07050efe3d7deaf915468d112dfcf5e426d0564aa3aa4a"},
+    {file = "greenlet-3.0.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b1660a15a446206c8545edc292ab5c48b91ff732f91b3d3b30d9a915d5ec4779"},
+    {file = "greenlet-3.0.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:813720bd57e193391dfe26f4871186cf460848b83df7e23e6bef698a7624b4c9"},
+    {file = "greenlet-3.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:aa15a2ec737cb609ed48902b45c5e4ff6044feb5dcdfcf6fa8482379190330d7"},
+    {file = "greenlet-3.0.0-cp310-universal2-macosx_11_0_x86_64.whl", hash = "sha256:7709fd7bb02b31908dc8fd35bfd0a29fc24681d5cc9ac1d64ad07f8d2b7db62f"},
+    {file = "greenlet-3.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:211ef8d174601b80e01436f4e6905aca341b15a566f35a10dd8d1e93f5dbb3b7"},
+    {file = "greenlet-3.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6512592cc49b2c6d9b19fbaa0312124cd4c4c8a90d28473f86f92685cc5fef8e"},
+    {file = "greenlet-3.0.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:871b0a8835f9e9d461b7fdaa1b57e3492dd45398e87324c047469ce2fc9f516c"},
+    {file = "greenlet-3.0.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b505fcfc26f4148551826a96f7317e02c400665fa0883fe505d4fcaab1dabfdd"},
+    {file = "greenlet-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:123910c58234a8d40eaab595bc56a5ae49bdd90122dde5bdc012c20595a94c14"},
+    {file = "greenlet-3.0.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:96d9ea57292f636ec851a9bb961a5cc0f9976900e16e5d5647f19aa36ba6366b"},
+    {file = "greenlet-3.0.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0b72b802496cccbd9b31acea72b6f87e7771ccfd7f7927437d592e5c92ed703c"},
+    {file = "greenlet-3.0.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:527cd90ba3d8d7ae7dceb06fda619895768a46a1b4e423bdb24c1969823b8362"},
+    {file = "greenlet-3.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:37f60b3a42d8b5499be910d1267b24355c495064f271cfe74bf28b17b099133c"},
+    {file = "greenlet-3.0.0-cp311-universal2-macosx_10_9_universal2.whl", hash = "sha256:c3692ecf3fe754c8c0f2c95ff19626584459eab110eaab66413b1e7425cd84e9"},
+    {file = "greenlet-3.0.0-cp312-cp312-macosx_13_0_arm64.whl", hash = "sha256:be557119bf467d37a8099d91fbf11b2de5eb1fd5fc5b91598407574848dc910f"},
+    {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73b2f1922a39d5d59cc0e597987300df3396b148a9bd10b76a058a2f2772fc04"},
+    {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d1e22c22f7826096ad503e9bb681b05b8c1f5a8138469b255eb91f26a76634f2"},
+    {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1d363666acc21d2c204dd8705c0e0457d7b2ee7a76cb16ffc099d6799744ac99"},
+    {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:334ef6ed8337bd0b58bb0ae4f7f2dcc84c9f116e474bb4ec250a8bb9bd797a66"},
+    {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6672fdde0fd1a60b44fb1751a7779c6db487e42b0cc65e7caa6aa686874e79fb"},
+    {file = "greenlet-3.0.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:952256c2bc5b4ee8df8dfc54fc4de330970bf5d79253c863fb5e6761f00dda35"},
+    {file = "greenlet-3.0.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:269d06fa0f9624455ce08ae0179430eea61085e3cf6457f05982b37fd2cefe17"},
+    {file = "greenlet-3.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9adbd8ecf097e34ada8efde9b6fec4dd2a903b1e98037adf72d12993a1c80b51"},
+    {file = "greenlet-3.0.0-cp312-universal2-macosx_10_9_universal2.whl", hash = "sha256:553d6fb2324e7f4f0899e5ad2c427a4579ed4873f42124beba763f16032959af"},
+    {file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c6b5ce7f40f0e2f8b88c28e6691ca6806814157ff05e794cdd161be928550f4c"},
+    {file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ecf94aa539e97a8411b5ea52fc6ccd8371be9550c4041011a091eb8b3ca1d810"},
+    {file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:80dcd3c938cbcac986c5c92779db8e8ce51a89a849c135172c88ecbdc8c056b7"},
+    {file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e52a712c38e5fb4fd68e00dc3caf00b60cb65634d50e32281a9d6431b33b4af1"},
+    {file = "greenlet-3.0.0-cp37-cp37m-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5539f6da3418c3dc002739cb2bb8d169056aa66e0c83f6bacae0cd3ac26b423"},
+    {file = "greenlet-3.0.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:343675e0da2f3c69d3fb1e894ba0a1acf58f481f3b9372ce1eb465ef93cf6fed"},
+    {file = "greenlet-3.0.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:abe1ef3d780de56defd0c77c5ba95e152f4e4c4e12d7e11dd8447d338b85a625"},
+    {file = "greenlet-3.0.0-cp37-cp37m-win32.whl", hash = "sha256:e693e759e172fa1c2c90d35dea4acbdd1d609b6936115d3739148d5e4cd11947"},
+    {file = "greenlet-3.0.0-cp37-cp37m-win_amd64.whl", hash = "sha256:bdd696947cd695924aecb3870660b7545a19851f93b9d327ef8236bfc49be705"},
+    {file = "greenlet-3.0.0-cp37-universal2-macosx_11_0_x86_64.whl", hash = "sha256:cc3e2679ea13b4de79bdc44b25a0c4fcd5e94e21b8f290791744ac42d34a0353"},
+    {file = "greenlet-3.0.0-cp38-cp38-macosx_11_0_universal2.whl", hash = "sha256:63acdc34c9cde42a6534518e32ce55c30f932b473c62c235a466469a710bfbf9"},
+    {file = "greenlet-3.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a1a6244ff96343e9994e37e5b4839f09a0207d35ef6134dce5c20d260d0302c"},
+    {file = "greenlet-3.0.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b822fab253ac0f330ee807e7485769e3ac85d5eef827ca224feaaefa462dc0d0"},
+    {file = "greenlet-3.0.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8060b32d8586e912a7b7dac2d15b28dbbd63a174ab32f5bc6d107a1c4143f40b"},
+    {file = "greenlet-3.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:621fcb346141ae08cb95424ebfc5b014361621b8132c48e538e34c3c93ac7365"},
+    {file = "greenlet-3.0.0-cp38-cp38-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6bb36985f606a7c49916eff74ab99399cdfd09241c375d5a820bb855dfb4af9f"},
+    {file = "greenlet-3.0.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:10b5582744abd9858947d163843d323d0b67be9432db50f8bf83031032bc218d"},
+    {file = "greenlet-3.0.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:f351479a6914fd81a55c8e68963609f792d9b067fb8a60a042c585a621e0de4f"},
+    {file = "greenlet-3.0.0-cp38-cp38-win32.whl", hash = "sha256:9de687479faec7db5b198cc365bc34addd256b0028956501f4d4d5e9ca2e240a"},
+    {file = "greenlet-3.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:3fd2b18432e7298fcbec3d39e1a0aa91ae9ea1c93356ec089421fabc3651572b"},
+    {file = "greenlet-3.0.0-cp38-universal2-macosx_11_0_x86_64.whl", hash = "sha256:3c0d36f5adc6e6100aedbc976d7428a9f7194ea79911aa4bf471f44ee13a9464"},
+    {file = "greenlet-3.0.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:4cd83fb8d8e17633ad534d9ac93719ef8937568d730ef07ac3a98cb520fd93e4"},
+    {file = "greenlet-3.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a5b2d4cdaf1c71057ff823a19d850ed5c6c2d3686cb71f73ae4d6382aaa7a06"},
+    {file = "greenlet-3.0.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e7dcdfad252f2ca83c685b0fa9fba00e4d8f243b73839229d56ee3d9d219314"},
+    {file = "greenlet-3.0.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c94e4e924d09b5a3e37b853fe5924a95eac058cb6f6fb437ebb588b7eda79870"},
+    {file = "greenlet-3.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad6fb737e46b8bd63156b8f59ba6cdef46fe2b7db0c5804388a2d0519b8ddb99"},
+    {file = "greenlet-3.0.0-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d55db1db455c59b46f794346efce896e754b8942817f46a1bada2d29446e305a"},
+    {file = "greenlet-3.0.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:56867a3b3cf26dc8a0beecdb4459c59f4c47cdd5424618c08515f682e1d46692"},
+    {file = "greenlet-3.0.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:9a812224a5fb17a538207e8cf8e86f517df2080c8ee0f8c1ed2bdaccd18f38f4"},
+    {file = "greenlet-3.0.0-cp39-cp39-win32.whl", hash = "sha256:0d3f83ffb18dc57243e0151331e3c383b05e5b6c5029ac29f754745c800f8ed9"},
+    {file = "greenlet-3.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:831d6f35037cf18ca5e80a737a27d822d87cd922521d18ed3dbc8a6967be50ce"},
+    {file = "greenlet-3.0.0-cp39-universal2-macosx_11_0_x86_64.whl", hash = "sha256:a048293392d4e058298710a54dfaefcefdf49d287cd33fb1f7d63d55426e4355"},
+    {file = "greenlet-3.0.0.tar.gz", hash = "sha256:19834e3f91f485442adc1ee440171ec5d9a4840a1f7bd5ed97833544719ce10b"},
 ]
 
 [package.extras]
@@ -751,13 +746,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.0.335"
+version = "0.0.327"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.335-py3-none-any.whl", hash = "sha256:f74c98366070a46953c071c69f6c01671a9437569c08406cace256ccaabdfcaf"},
-    {file = "langchain-0.0.335.tar.gz", hash = "sha256:93136fe6cc9ac06a80ccf7cf581e58af5cfcc31fef1083b30165df9a9bc53f5d"},
+    {file = "langchain-0.0.327-py3-none-any.whl", hash = "sha256:21835600e1ab11e2a939d9e473c13ed51402a3b75418ca02689877a5764da398"},
+    {file = "langchain-0.0.327.tar.gz", hash = "sha256:2710fba0c0735d1a63327cad83387571adc457fe75075c70335e8ea628f0a8a2"},
 ]
 
 [package.dependencies]
@@ -766,7 +761,7 @@ anyio = "<4.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.0.63,<0.1.0"
+langsmith = ">=0.0.52,<0.1.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -809,21 +804,20 @@ uvicorn = ">=0.23.2,<0.24.0"
 
 [[package]]
 name = "langserve"
-version = "0.0.27"
+version = "0.0.24"
 description = ""
 optional = false
 python-versions = ">=3.8.1,<4.0.0"
 files = [
-    {file = "langserve-0.0.27-py3-none-any.whl", hash = "sha256:7d9af0dc96edad243f22b60bfa155aaf324164f3e608bd3006e97180a40d4770"},
-    {file = "langserve-0.0.27.tar.gz", hash = "sha256:c8ad5acf3c2d46ceea66b3c85615732b267cefbfc0924267f2326db9bb019736"},
+    {file = "langserve-0.0.24-py3-none-any.whl", hash = "sha256:7c3f65b2585f39e5ad0b4ae8609eac9b59079ed5a7cfd8ef96d20a3ee5c20309"},
+    {file = "langserve-0.0.24.tar.gz", hash = "sha256:aba25983943b2408a8bdf2d7a324da58d3ab84ce4ea6b134bc5ccd969e522725"},
 ]
 
 [package.dependencies]
 fastapi = {version = ">=0.90.1", optional = true, markers = "extra == \"server\" or extra == \"all\""}
 httpx = ">=0.23.0"
 httpx-sse = {version = ">=0.3.1", optional = true, markers = "extra == \"client\" or extra == \"all\""}
-langchain = ">=0.0.333"
-orjson = ">=2"
+langchain = ">=0.0.322"
 pydantic = ">=1"
 sse-starlette = {version = ">=1.3.0,<2.0.0", optional = true, markers = "extra == \"server\" or extra == \"all\""}
 
@@ -834,13 +828,13 @@ server = ["fastapi (>=0.90.1)", "sse-starlette (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.0.64"
+version = "0.0.54"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.64-py3-none-any.whl", hash = "sha256:461acdcd8332d1325c16dc57e8a2d5ec9d1578490a4eaabe14db74db74ceaf21"},
-    {file = "langsmith-0.0.64.tar.gz", hash = "sha256:e78c02501c2cff24fff7bd2d28ff3765b21675c7f0fcf6a09932bc218603c36e"},
+    {file = "langsmith-0.0.54-py3-none-any.whl", hash = "sha256:55eca5967cadb661a49ad32aecda48a824fadef202ca384575209a9d6f823b74"},
+    {file = "langsmith-0.0.54.tar.gz", hash = "sha256:76c8e34b4d10ad93541107138089635829f9d60601a7f6bddf5ba582d178e521"},
 ]
 
 [package.dependencies]
@@ -1053,84 +1047,25 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.2.4"
-description = "The official Python library for the openai API"
+version = "0.28.1"
+description = "Python client library for the OpenAI API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.2.4-py3-none-any.whl", hash = "sha256:53927a2ca276eec0a0efdc1ae829f74a51f49b7d3e14cc6f820aeafb0abfd802"},
-    {file = "openai-1.2.4.tar.gz", hash = "sha256:d99a474049376be431d9b4dec3a5c895dd76e19165748c5944e80b7905d1b1ff"},
+    {file = "openai-0.28.1-py3-none-any.whl", hash = "sha256:d18690f9e3d31eedb66b57b88c2165d760b24ea0a01f150dd3f068155088ce68"},
+    {file = "openai-0.28.1.tar.gz", hash = "sha256:4be1dad329a65b4ce1a660fe6d5431b438f429b5855c883435f0f7fcb6d2dcc8"},
 ]
 
 [package.dependencies]
-anyio = ">=3.5.0,<4"
-distro = ">=1.7.0,<2"
-httpx = ">=0.23.0,<1"
-pydantic = ">=1.9.0,<3"
-tqdm = ">4"
-typing-extensions = ">=4.5,<5"
+aiohttp = "*"
+requests = ">=2.20"
+tqdm = "*"
 
 [package.extras]
-datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
-
-[[package]]
-name = "orjson"
-version = "3.9.10"
-description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "orjson-3.9.10-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:c18a4da2f50050a03d1da5317388ef84a16013302a5281d6f64e4a3f406aabc4"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5148bab4d71f58948c7c39d12b14a9005b6ab35a0bdf317a8ade9a9e4d9d0bd5"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4cf7837c3b11a2dfb589f8530b3cff2bd0307ace4c301e8997e95c7468c1378e"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c62b6fa2961a1dcc51ebe88771be5319a93fd89bd247c9ddf732bc250507bc2b"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:deeb3922a7a804755bbe6b5be9b312e746137a03600f488290318936c1a2d4dc"},
-    {file = "orjson-3.9.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1234dc92d011d3554d929b6cf058ac4a24d188d97be5e04355f1b9223e98bbe9"},
-    {file = "orjson-3.9.10-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:06ad5543217e0e46fd7ab7ea45d506c76f878b87b1b4e369006bdb01acc05a83"},
-    {file = "orjson-3.9.10-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4fd72fab7bddce46c6826994ce1e7de145ae1e9e106ebb8eb9ce1393ca01444d"},
-    {file = "orjson-3.9.10-cp310-none-win32.whl", hash = "sha256:b5b7d4a44cc0e6ff98da5d56cde794385bdd212a86563ac321ca64d7f80c80d1"},
-    {file = "orjson-3.9.10-cp310-none-win_amd64.whl", hash = "sha256:61804231099214e2f84998316f3238c4c2c4aaec302df12b21a64d72e2a135c7"},
-    {file = "orjson-3.9.10-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:cff7570d492bcf4b64cc862a6e2fb77edd5e5748ad715f487628f102815165e9"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed8bc367f725dfc5cabeed1ae079d00369900231fbb5a5280cf0736c30e2adf7"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c812312847867b6335cfb264772f2a7e85b3b502d3a6b0586aa35e1858528ab1"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9edd2856611e5050004f4722922b7b1cd6268da34102667bd49d2a2b18bafb81"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:674eb520f02422546c40401f4efaf8207b5e29e420c17051cddf6c02783ff5ca"},
-    {file = "orjson-3.9.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d0dc4310da8b5f6415949bd5ef937e60aeb0eb6b16f95041b5e43e6200821fb"},
-    {file = "orjson-3.9.10-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e99c625b8c95d7741fe057585176b1b8783d46ed4b8932cf98ee145c4facf499"},
-    {file = "orjson-3.9.10-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:ec6f18f96b47299c11203edfbdc34e1b69085070d9a3d1f302810cc23ad36bf3"},
-    {file = "orjson-3.9.10-cp311-none-win32.whl", hash = "sha256:ce0a29c28dfb8eccd0f16219360530bc3cfdf6bf70ca384dacd36e6c650ef8e8"},
-    {file = "orjson-3.9.10-cp311-none-win_amd64.whl", hash = "sha256:cf80b550092cc480a0cbd0750e8189247ff45457e5a023305f7ef1bcec811616"},
-    {file = "orjson-3.9.10-cp312-cp312-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:602a8001bdf60e1a7d544be29c82560a7b49319a0b31d62586548835bbe2c862"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f295efcd47b6124b01255d1491f9e46f17ef40d3d7eabf7364099e463fb45f0f"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:92af0d00091e744587221e79f68d617b432425a7e59328ca4c496f774a356071"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c5a02360e73e7208a872bf65a7554c9f15df5fe063dc047f79738998b0506a14"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:858379cbb08d84fe7583231077d9a36a1a20eb72f8c9076a45df8b083724ad1d"},
-    {file = "orjson-3.9.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666c6fdcaac1f13eb982b649e1c311c08d7097cbda24f32612dae43648d8db8d"},
-    {file = "orjson-3.9.10-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3fb205ab52a2e30354640780ce4587157a9563a68c9beaf52153e1cea9aa0921"},
-    {file = "orjson-3.9.10-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:7ec960b1b942ee3c69323b8721df2a3ce28ff40e7ca47873ae35bfafeb4555ca"},
-    {file = "orjson-3.9.10-cp312-none-win_amd64.whl", hash = "sha256:3e892621434392199efb54e69edfff9f699f6cc36dd9553c5bf796058b14b20d"},
-    {file = "orjson-3.9.10-cp38-cp38-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:8b9ba0ccd5a7f4219e67fbbe25e6b4a46ceef783c42af7dbc1da548eb28b6531"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e2ecd1d349e62e3960695214f40939bbfdcaeaaa62ccc638f8e651cf0970e5f"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7f433be3b3f4c66016d5a20e5b4444ef833a1f802ced13a2d852c637f69729c1"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4689270c35d4bb3102e103ac43c3f0b76b169760aff8bcf2d401a3e0e58cdb7f"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4bd176f528a8151a6efc5359b853ba3cc0e82d4cd1fab9c1300c5d957dc8f48c"},
-    {file = "orjson-3.9.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a2ce5ea4f71681623f04e2b7dadede3c7435dfb5e5e2d1d0ec25b35530e277b"},
-    {file = "orjson-3.9.10-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:49f8ad582da6e8d2cf663c4ba5bf9f83cc052570a3a767487fec6af839b0e777"},
-    {file = "orjson-3.9.10-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:2a11b4b1a8415f105d989876a19b173f6cdc89ca13855ccc67c18efbd7cbd1f8"},
-    {file = "orjson-3.9.10-cp38-none-win32.whl", hash = "sha256:a353bf1f565ed27ba71a419b2cd3db9d6151da426b61b289b6ba1422a702e643"},
-    {file = "orjson-3.9.10-cp38-none-win_amd64.whl", hash = "sha256:e28a50b5be854e18d54f75ef1bb13e1abf4bc650ab9d635e4258c58e71eb6ad5"},
-    {file = "orjson-3.9.10-cp39-cp39-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:ee5926746232f627a3be1cc175b2cfad24d0170d520361f4ce3fa2fd83f09e1d"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a73160e823151f33cdc05fe2cea557c5ef12fdf276ce29bb4f1c571c8368a60"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c338ed69ad0b8f8f8920c13f529889fe0771abbb46550013e3c3d01e5174deef"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5869e8e130e99687d9e4be835116c4ebd83ca92e52e55810962446d841aba8de"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d2c1e559d96a7f94a4f581e2a32d6d610df5840881a8cba8f25e446f4d792df3"},
-    {file = "orjson-3.9.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81a3a3a72c9811b56adf8bcc829b010163bb2fc308877e50e9910c9357e78521"},
-    {file = "orjson-3.9.10-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7f8fb7f5ecf4f6355683ac6881fd64b5bb2b8a60e3ccde6ff799e48791d8f864"},
-    {file = "orjson-3.9.10-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c943b35ecdf7123b2d81d225397efddf0bce2e81db2f3ae633ead38e85cd5ade"},
-    {file = "orjson-3.9.10-cp39-none-win32.whl", hash = "sha256:fb0b361d73f6b8eeceba47cd37070b5e6c9de5beaeaa63a1cb35c7e1a73ef088"},
-    {file = "orjson-3.9.10-cp39-none-win_amd64.whl", hash = "sha256:b90f340cb6397ec7a854157fac03f0c82b744abdd1c0941a024c3c29d1340aff"},
-    {file = "orjson-3.9.10.tar.gz", hash = "sha256:9ebbdbd6a046c304b1845e96fbcc5559cd296b4dfd3ad2509e33c4d9ce07d6a1"},
-]
+datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+dev = ["black (>=21.6b0,<22.0)", "pytest (==6.*)", "pytest-asyncio", "pytest-mock"]
+embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"]
+wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
 
 [[package]]
 name = "packaging"
@@ -1192,6 +1127,8 @@ files = [
     {file = "psycopg2-2.9.9-cp310-cp310-win_amd64.whl", hash = "sha256:426f9f29bde126913a20a96ff8ce7d73fd8a216cfb323b1f04da402d452853c3"},
     {file = "psycopg2-2.9.9-cp311-cp311-win32.whl", hash = "sha256:ade01303ccf7ae12c356a5e10911c9e1c51136003a9a1d92f7aa9d010fb98372"},
     {file = "psycopg2-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:121081ea2e76729acfb0673ff33755e8703d45e926e416cb59bae3a86c6a4981"},
+    {file = "psycopg2-2.9.9-cp312-cp312-win32.whl", hash = "sha256:d735786acc7dd25815e89cc4ad529a43af779db2e25aa7c626de864127e5a024"},
+    {file = "psycopg2-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win32.whl", hash = "sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa"},
     {file = "psycopg2-2.9.9-cp37-cp37m-win_amd64.whl", hash = "sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a"},
     {file = "psycopg2-2.9.9-cp38-cp38-win32.whl", hash = "sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"},
@@ -1203,18 +1140,18 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.5.0"
+version = "2.4.2"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pydantic-2.5.0-py3-none-any.whl", hash = "sha256:7ce6e766c456ad026fe5712f7bcf036efc34bd5d107b3e669ef7ea01b3a9050c"},
-    {file = "pydantic-2.5.0.tar.gz", hash = "sha256:69bd6fb62d2d04b7055f59a396993486a2ee586c43a0b89231ce0000de07627c"},
+    {file = "pydantic-2.4.2-py3-none-any.whl", hash = "sha256:bc3ddf669d234f4220e6e1c4d96b061abe0998185a8d7855c0126782b7abc8c1"},
+    {file = "pydantic-2.4.2.tar.gz", hash = "sha256:94f336138093a5d7f426aac732dcfe7ab4eb4da243c88f891d65deb4a2556ee7"},
 ]
 
 [package.dependencies]
 annotated-types = ">=0.4.0"
-pydantic-core = "2.14.1"
+pydantic-core = "2.10.1"
 typing-extensions = ">=4.6.1"
 
 [package.extras]
@@ -1222,112 +1159,117 @@ email = ["email-validator (>=2.0.0)"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.14.1"
+version = "2.10.1"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pydantic_core-2.14.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:812beca1dcb2b722cccc7e9c620bd972cbc323321194ec2725eab3222e6ac573"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a2ccdc53cb88e51c7d47d74c59630d7be844428f6b8d463055ffad6f0392d8da"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd937733bf2fe7d6a8bf208c12741f1f730b7bf5636033877767a75093c29b8a"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:581bb606a31749a00796f5257947a0968182d7fe91e1dada41f06aeb6bfbc91a"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aadf74a40a7ae49c3c1aa7d32334fe94f4f968e21dd948e301bb4ed431fb2412"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b89821a2c77cc1b8f2c1fc3aacd6a3ecc5df8f7e518dc3f18aef8c4dcf66003d"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49ee28d65f506b2858a60745cc974ed005298ebab12693646b97641dd7c99c35"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:97246f896b4df7fd84caa8a75a67abb95f94bc0b547665bf0889e3262b060399"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:1185548665bc61bbab0dc78f10c8eafa0db0aa1e920fe9a451b77782b10a65cc"},
-    {file = "pydantic_core-2.14.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2a7d08b39fac97540fba785fce3b21ee01a81f081a07a4d031efd791da6666f9"},
-    {file = "pydantic_core-2.14.1-cp310-none-win32.whl", hash = "sha256:0a8c8daf4e3aa3aeb98e3638fc3d58a359738f3d12590b2474c6bb64031a0764"},
-    {file = "pydantic_core-2.14.1-cp310-none-win_amd64.whl", hash = "sha256:4f0788699a92d604f348e9c1ac5e97e304e97127ba8325c7d0af88dcc7d35bd3"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:2be018a84995b6be1bbd40d6064395dbf71592a981169cf154c0885637f5f54a"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fc3227408808ba7df8e95eb1d8389f4ba2203bed8240b308de1d7ae66d828f24"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42d5d0e9bbb50481a049bd0203224b339d4db04006b78564df2b782e2fd16ebc"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bc6a4ea9f88a810cb65ccae14404da846e2a02dd5c0ad21dee712ff69d142638"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d312ad20e3c6d179cb97c42232b53111bcd8dcdd5c1136083db9d6bdd489bc73"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:679cc4e184f213c8227862e57340d12fd4d4d19dc0e3ddb0f653f86f01e90f94"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:101df420e954966868b8bc992aefed5fa71dd1f2755104da62ee247abab28e2f"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c964c0cc443d6c08a2347c0e5c1fc2d85a272dc66c1a6f3cde4fc4843882ada4"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:8276bbab68a9dbe721da92d19cbc061f76655248fe24fb63969d0c3e0e5755e7"},
-    {file = "pydantic_core-2.14.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:12163197fec7c95751a3c71b36dcc1909eed9959f011ffc79cc8170a6a74c826"},
-    {file = "pydantic_core-2.14.1-cp311-none-win32.whl", hash = "sha256:b8ff0302518dcd001bd722bbe342919c29e5066c7eda86828fe08cdc112668b8"},
-    {file = "pydantic_core-2.14.1-cp311-none-win_amd64.whl", hash = "sha256:59fa83873223f856d898452c6162a390af4297756f6ba38493a67533387d85d9"},
-    {file = "pydantic_core-2.14.1-cp311-none-win_arm64.whl", hash = "sha256:798590d38c9381f07c48d13af1f1ef337cebf76ee452fcec5deb04aceced51c7"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:587d75aec9ae50d0d63788cec38bf13c5128b3fc1411aa4b9398ebac884ab179"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:26242e3593d4929123615bd9365dd86ef79b7b0592d64a96cd11fd83c69c9f34"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5879ac4791508d8f0eb7dec71ff8521855180688dac0c55f8c99fc4d1a939845"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ad9ea86f5fc50f1b62c31184767fe0cacaa13b54fe57d38898c3776d30602411"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:102ac85a775e77821943ae38da9634ddd774b37a8d407181b4f7b05cdfb36b55"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2459cc06572730e079ec1e694e8f68c99d977b40d98748ae72ff11ef21a56b0b"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:217dcbfaf429a9b8f1d54eb380908b9c778e78f31378283b30ba463c21e89d5d"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9d59e0d7cdfe8ed1d4fcd28aad09625c715dc18976c7067e37d8a11b06f4be3e"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e2be646a5155d408e68b560c0553e8a83dc7b9f90ec6e5a2fc3ff216719385db"},
-    {file = "pydantic_core-2.14.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ffba979801e3931a19cd30ed2049450820effe8f152aaa317e2fd93795d318d7"},
-    {file = "pydantic_core-2.14.1-cp312-none-win32.whl", hash = "sha256:132b40e479cb5cebbbb681f77aaceabbc8355df16c9124cff1d4060ada83cde2"},
-    {file = "pydantic_core-2.14.1-cp312-none-win_amd64.whl", hash = "sha256:744b807fe2733b6da3b53e8ad93e8b3ea3ee3dfc3abece4dd2824cc1f39aa343"},
-    {file = "pydantic_core-2.14.1-cp312-none-win_arm64.whl", hash = "sha256:24ba48f9d0b8d64fc5e42e1600366c3d7db701201294989aebdaca23110c02ab"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:ba55d73a2df4771b211d0bcdea8b79454980a81ed34a1d77a19ddcc81f98c895"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:e905014815687d88cbb14bbc0496420526cf20d49f20606537d87646b70f1046"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:443dc5eede7fa76b2370213e0abe881eb17c96f7d694501853c11d5d56916602"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:abae6fd5504e5e438e4f6f739f8364fd9ff5a5cdca897e68363e2318af90bc28"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9486e27bb3f137f33e2315be2baa0b0b983dae9e2f5f5395240178ad8e644728"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:69df82892ff00491d673b1929538efb8c8d68f534fdc6cb7fd3ac8a5852b9034"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:184ff7b30c3f60e1b775378c060099285fd4b5249271046c9005f8b247b39377"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3d5b2a4b3c10cad0615670cab99059441ff42e92cf793a0336f4bc611e895204"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:871c641a83719caaa856a11dcc61c5e5b35b0db888e1a0d338fe67ce744575e2"},
-    {file = "pydantic_core-2.14.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:1e7208946ea9b27a8cef13822c339d4ae96e45952cc01fc4a91c7f1cb0ae2861"},
-    {file = "pydantic_core-2.14.1-cp37-none-win32.whl", hash = "sha256:b4ff385a525017f5adf6066d7f9fb309f99ade725dcf17ed623dc7dce1f85d9f"},
-    {file = "pydantic_core-2.14.1-cp37-none-win_amd64.whl", hash = "sha256:c7411cd06afeb263182e38c6ca5b4f5fe4f20d91466ad7db0cd6af453a02edec"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:2871daf5b2823bf77bf7d3d43825e5d904030c155affdf84b21a00a2e00821d2"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7977e261cac5f99873dc2c6f044315d09b19a71c4246560e1e67593889a90978"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5a111f9158555582deadd202a60bd7803b6c68f406391b7cf6905adf0af6811"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ac417312bf6b7a0223ba73fb12e26b2854c93bf5b1911f7afef6d24c379b22aa"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c36987f5eb2a7856b5f5feacc3be206b4d1852a6ce799f6799dd9ffb0cba56ae"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6e98227eb02623d57e1fd061788837834b68bb995a869565211b9abf3de4bf4"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:023b6d7ec4e97890b28eb2ee24413e69a6d48de4e8b75123957edd5432f4eeb3"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6015beb28deb5306049ecf2519a59627e9e050892927850a884df6d5672f8c7d"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:3f48d4afd973abbd65266ac24b24de1591116880efc7729caf6b6b94a9654c9e"},
-    {file = "pydantic_core-2.14.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:28734bcfb8fc5b03293dec5eb5ea73b32ff767f6ef79a31f6e41dad2f5470270"},
-    {file = "pydantic_core-2.14.1-cp38-none-win32.whl", hash = "sha256:3303113fdfaca927ef11e0c5f109e2ec196c404f9d7ba5f8ddb63cdf287ea159"},
-    {file = "pydantic_core-2.14.1-cp38-none-win_amd64.whl", hash = "sha256:144f2c1d5579108b6ed1193fcc9926124bd4142b0f7020a7744980d1235c8a40"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:893bf4fb9bfb9c4639bc12f3de323325ada4c6d60e478d5cded65453e9364890"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:052d8731aaf844f91fe4cd3faf28983b109a5865b3a256ec550b80a5689ead87"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb1c6ecb53e4b907ee8486f453dd940b8cbb509946e2b671e3bf807d310a96fc"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:94cf6d0274eb899d39189144dcf52814c67f9b0fd196f211420d9aac793df2da"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:36c3bf96f803e207a80dbcb633d82b98ff02a9faa76dd446e969424dec8e2b9f"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fb290491f1f0786a7da4585250f1feee200fc17ff64855bdd7c42fb54526fa29"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6590ed9d13eb51b28ea17ddcc6c8dbd6050b4eb589d497105f0e13339f223b72"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:69cd74e55a5326d920e7b46daa2d81c2bdb8bcf588eafb2330d981297b742ddc"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d965bdb50725a805b083f5f58d05669a85705f50a6a864e31b545c589290ee31"},
-    {file = "pydantic_core-2.14.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ca942a2dc066ca5e04c27feaa8dfb9d353ddad14c6641660c565149186095343"},
-    {file = "pydantic_core-2.14.1-cp39-none-win32.whl", hash = "sha256:72c2ef3787c3b577e5d6225d73a77167b942d12cef3c1fbd5e74e55b7f881c36"},
-    {file = "pydantic_core-2.14.1-cp39-none-win_amd64.whl", hash = "sha256:55713d155da1e508083c4b08d0b1ad2c3054f68b8ef7eb3d3864822e456f0bb5"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:53efe03cc383a83660cfdda6a3cb40ee31372cedea0fde0b2a2e55e838873ab6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:f523e116879bc6714e61d447ce934676473b068069dce6563ea040381dc7a257"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85bb66d661be51b2cba9ca06759264b3469d2dbb53c3e6effb3f05fec6322be6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f53a3ccdc30234cb4342cec541e3e6ed87799c7ca552f0b5f44e3967a5fed526"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:1bfb63821ada76719ffcd703fc40dd57962e0d8c253e3c565252e6de6d3e0bc6"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e2c689439f262c29cf3fcd5364da1e64d8600facecf9eabea8643b8755d2f0de"},
-    {file = "pydantic_core-2.14.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a15f6e5588f7afb7f6fc4b0f4ff064749e515d34f34c666ed6e37933873d8ad8"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:f1a30eef060e21af22c7d23349f1028de0611f522941c80efa51c05a63142c62"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16f4a7e1ec6b3ea98a1e108a2739710cd659d68b33fbbeaba066202cab69c7b6"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fd80a2d383940eec3db6a5b59d1820f947317acc5c75482ff8d79bf700f8ad6a"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:a68a36d71c7f638dda6c9e6b67f6aabf3fa1471b198d246457bfdc7c777cdeb7"},
-    {file = "pydantic_core-2.14.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ebc79120e105e4bcd7865f369e3b9dbabb0d492d221e1a7f62a3e8e292550278"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:c8c466facec2ccdf025b0b1455b18f2c3d574d5f64d24df905d3d7b8f05d5f4e"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:b91b5ec423e88caa16777094c4b2b97f11453283e7a837e5e5e1b886abba1251"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:130e49aa0cb316f743bc7792c36aefa39fc2221312f1d4b333b19edbdd71f2b1"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f483467c046f549572f8aca3b7128829e09ae3a9fe933ea421f7cb7c58120edb"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:dee4682bd7947afc682d342a8d65ad1834583132383f8e801601a8698cb8d17a"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:8d927d042c0ef04607ee7822828b208ab045867d20477ec6593d612156798547"},
-    {file = "pydantic_core-2.14.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5a1570875eb0d1479fb2270ed80c88c231aaaf68b0c3f114f35e7fb610435e4f"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:cb2fd3ab67558eb16aecfb4f2db4febb4d37dc74e6b8613dc2e7160fb58158a9"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a7991f25b98038252363a03e6a9fe92e60fe390fda2631d238dc3b0e396632f8"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5b45b7be9f99991405ecd6f6172fb6798908a8097106ae78d5cc5cc15121bad9"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:51506e7652a2ef1d1cf763c4b51b972ff4568d1dddc96ca83931a6941f5e6389"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:66dc0e63349ec39c1ea66622aa5c2c1f84382112afd3ab2fa0cca4fb01f7db39"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:8e17f0c3ba4cb07faa0038a59ce162de584ed48ba645c8d05a5de1e40d4c21e7"},
-    {file = "pydantic_core-2.14.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d983222223f63e323a5f497f5b85e211557a5d8fb670dc88f343784502b466ba"},
-    {file = "pydantic_core-2.14.1.tar.gz", hash = "sha256:0d82a6ee815388a362885186e431fac84c7a06623bc136f508e9f88261d8cadb"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:d64728ee14e667ba27c66314b7d880b8eeb050e58ffc5fec3b7a109f8cddbd63"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:48525933fea744a3e7464c19bfede85df4aba79ce90c60b94d8b6e1eddd67096"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ef337945bbd76cce390d1b2496ccf9f90b1c1242a3a7bc242ca4a9fc5993427a"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a1392e0638af203cee360495fd2cfdd6054711f2db5175b6e9c3c461b76f5175"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0675ba5d22de54d07bccde38997e780044dcfa9a71aac9fd7d4d7a1d2e3e65f7"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:128552af70a64660f21cb0eb4876cbdadf1a1f9d5de820fed6421fa8de07c893"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f6e6aed5818c264412ac0598b581a002a9f050cb2637a84979859e70197aa9e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ecaac27da855b8d73f92123e5f03612b04c5632fd0a476e469dfc47cd37d6b2e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b3c01c2fb081fced3bbb3da78510693dc7121bb893a1f0f5f4b48013201f362e"},
+    {file = "pydantic_core-2.10.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:92f675fefa977625105708492850bcbc1182bfc3e997f8eecb866d1927c98ae6"},
+    {file = "pydantic_core-2.10.1-cp310-none-win32.whl", hash = "sha256:420a692b547736a8d8703c39ea935ab5d8f0d2573f8f123b0a294e49a73f214b"},
+    {file = "pydantic_core-2.10.1-cp310-none-win_amd64.whl", hash = "sha256:0880e239827b4b5b3e2ce05e6b766a7414e5f5aedc4523be6b68cfbc7f61c5d0"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:073d4a470b195d2b2245d0343569aac7e979d3a0dcce6c7d2af6d8a920ad0bea"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:600d04a7b342363058b9190d4e929a8e2e715c5682a70cc37d5ded1e0dd370b4"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39215d809470f4c8d1881758575b2abfb80174a9e8daf8f33b1d4379357e417c"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eeb3d3d6b399ffe55f9a04e09e635554012f1980696d6b0aca3e6cf42a17a03b"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a7a7902bf75779bc12ccfc508bfb7a4c47063f748ea3de87135d433a4cca7a2f"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3625578b6010c65964d177626fde80cf60d7f2e297d56b925cb5cdeda6e9925a"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:caa48fc31fc7243e50188197b5f0c4228956f97b954f76da157aae7f67269ae8"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:07ec6d7d929ae9c68f716195ce15e745b3e8fa122fc67698ac6498d802ed0fa4"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e6f31a17acede6a8cd1ae2d123ce04d8cca74056c9d456075f4f6f85de055607"},
+    {file = "pydantic_core-2.10.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d8f1ebca515a03e5654f88411420fea6380fc841d1bea08effb28184e3d4899f"},
+    {file = "pydantic_core-2.10.1-cp311-none-win32.whl", hash = "sha256:6db2eb9654a85ada248afa5a6db5ff1cf0f7b16043a6b070adc4a5be68c716d6"},
+    {file = "pydantic_core-2.10.1-cp311-none-win_amd64.whl", hash = "sha256:4a5be350f922430997f240d25f8219f93b0c81e15f7b30b868b2fddfc2d05f27"},
+    {file = "pydantic_core-2.10.1-cp311-none-win_arm64.whl", hash = "sha256:5fdb39f67c779b183b0c853cd6b45f7db84b84e0571b3ef1c89cdb1dfc367325"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:b1f22a9ab44de5f082216270552aa54259db20189e68fc12484873d926426921"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8572cadbf4cfa95fb4187775b5ade2eaa93511f07947b38f4cd67cf10783b118"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:db9a28c063c7c00844ae42a80203eb6d2d6bbb97070cfa00194dff40e6f545ab"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0e2a35baa428181cb2270a15864ec6286822d3576f2ed0f4cd7f0c1708472aff"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:05560ab976012bf40f25d5225a58bfa649bb897b87192a36c6fef1ab132540d7"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d6495008733c7521a89422d7a68efa0a0122c99a5861f06020ef5b1f51f9ba7c"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14ac492c686defc8e6133e3a2d9eaf5261b3df26b8ae97450c1647286750b901"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8282bab177a9a3081fd3d0a0175a07a1e2bfb7fcbbd949519ea0980f8a07144d"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:aafdb89fdeb5fe165043896817eccd6434aee124d5ee9b354f92cd574ba5e78f"},
+    {file = "pydantic_core-2.10.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:f6defd966ca3b187ec6c366604e9296f585021d922e666b99c47e78738b5666c"},
+    {file = "pydantic_core-2.10.1-cp312-none-win32.whl", hash = "sha256:7c4d1894fe112b0864c1fa75dffa045720a194b227bed12f4be7f6045b25209f"},
+    {file = "pydantic_core-2.10.1-cp312-none-win_amd64.whl", hash = "sha256:5994985da903d0b8a08e4935c46ed8daf5be1cf217489e673910951dc533d430"},
+    {file = "pydantic_core-2.10.1-cp312-none-win_arm64.whl", hash = "sha256:0d8a8adef23d86d8eceed3e32e9cca8879c7481c183f84ed1a8edc7df073af94"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:9badf8d45171d92387410b04639d73811b785b5161ecadabf056ea14d62d4ede"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:ebedb45b9feb7258fac0a268a3f6bec0a2ea4d9558f3d6f813f02ff3a6dc6698"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cfe1090245c078720d250d19cb05d67e21a9cd7c257698ef139bc41cf6c27b4f"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e357571bb0efd65fd55f18db0a2fb0ed89d0bb1d41d906b138f088933ae618bb"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b3dcd587b69bbf54fc04ca157c2323b8911033e827fffaecf0cafa5a892a0904"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c120c9ce3b163b985a3b966bb701114beb1da4b0468b9b236fc754783d85aa3"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15d6bca84ffc966cc9976b09a18cf9543ed4d4ecbd97e7086f9ce9327ea48891"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5cabb9710f09d5d2e9e2748c3e3e20d991a4c5f96ed8f1132518f54ab2967221"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:82f55187a5bebae7d81d35b1e9aaea5e169d44819789837cdd4720d768c55d15"},
+    {file = "pydantic_core-2.10.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:1d40f55222b233e98e3921df7811c27567f0e1a4411b93d4c5c0f4ce131bc42f"},
+    {file = "pydantic_core-2.10.1-cp37-none-win32.whl", hash = "sha256:14e09ff0b8fe6e46b93d36a878f6e4a3a98ba5303c76bb8e716f4878a3bee92c"},
+    {file = "pydantic_core-2.10.1-cp37-none-win_amd64.whl", hash = "sha256:1396e81b83516b9d5c9e26a924fa69164156c148c717131f54f586485ac3c15e"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:6835451b57c1b467b95ffb03a38bb75b52fb4dc2762bb1d9dbed8de31ea7d0fc"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b00bc4619f60c853556b35f83731bd817f989cba3e97dc792bb8c97941b8053a"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0fa467fd300a6f046bdb248d40cd015b21b7576c168a6bb20aa22e595c8ffcdd"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d99277877daf2efe074eae6338453a4ed54a2d93fb4678ddfe1209a0c93a2468"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fa7db7558607afeccb33c0e4bf1c9a9a835e26599e76af6fe2fcea45904083a6"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:aad7bd686363d1ce4ee930ad39f14e1673248373f4a9d74d2b9554f06199fb58"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:443fed67d33aa85357464f297e3d26e570267d1af6fef1c21ca50921d2976302"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:042462d8d6ba707fd3ce9649e7bf268633a41018d6a998fb5fbacb7e928a183e"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ecdbde46235f3d560b18be0cb706c8e8ad1b965e5c13bbba7450c86064e96561"},
+    {file = "pydantic_core-2.10.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ed550ed05540c03f0e69e6d74ad58d026de61b9eaebebbaaf8873e585cbb18de"},
+    {file = "pydantic_core-2.10.1-cp38-none-win32.whl", hash = "sha256:8cdbbd92154db2fec4ec973d45c565e767ddc20aa6dbaf50142676484cbff8ee"},
+    {file = "pydantic_core-2.10.1-cp38-none-win_amd64.whl", hash = "sha256:9f6f3e2598604956480f6c8aa24a3384dbf6509fe995d97f6ca6103bb8c2534e"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:655f8f4c8d6a5963c9a0687793da37b9b681d9ad06f29438a3b2326d4e6b7970"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e570ffeb2170e116a5b17e83f19911020ac79d19c96f320cbfa1fa96b470185b"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:64322bfa13e44c6c30c518729ef08fda6026b96d5c0be724b3c4ae4da939f875"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:485a91abe3a07c3a8d1e082ba29254eea3e2bb13cbbd4351ea4e5a21912cc9b0"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f7c2b8eb9fc872e68b46eeaf835e86bccc3a58ba57d0eedc109cbb14177be531"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a5cb87bdc2e5f620693148b5f8f842d293cae46c5f15a1b1bf7ceeed324a740c"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:25bd966103890ccfa028841a8f30cebcf5875eeac8c4bde4fe221364c92f0c9a"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f323306d0556351735b54acbf82904fe30a27b6a7147153cbe6e19aaaa2aa429"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0c27f38dc4fbf07b358b2bc90edf35e82d1703e22ff2efa4af4ad5de1b3833e7"},
+    {file = "pydantic_core-2.10.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:f1365e032a477c1430cfe0cf2856679529a2331426f8081172c4a74186f1d595"},
+    {file = "pydantic_core-2.10.1-cp39-none-win32.whl", hash = "sha256:a1c311fd06ab3b10805abb72109f01a134019739bd3286b8ae1bc2fc4e50c07a"},
+    {file = "pydantic_core-2.10.1-cp39-none-win_amd64.whl", hash = "sha256:ae8a8843b11dc0b03b57b52793e391f0122e740de3df1474814c700d2622950a"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:d43002441932f9a9ea5d6f9efaa2e21458221a3a4b417a14027a1d530201ef1b"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:fcb83175cc4936a5425dde3356f079ae03c0802bbdf8ff82c035f8a54b333521"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:962ed72424bf1f72334e2f1e61b68f16c0e596f024ca7ac5daf229f7c26e4208"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2cf5bb4dd67f20f3bbc1209ef572a259027c49e5ff694fa56bed62959b41e1f9"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e544246b859f17373bed915182ab841b80849ed9cf23f1f07b73b7c58baee5fb"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:c0877239307b7e69d025b73774e88e86ce82f6ba6adf98f41069d5b0b78bd1bf"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:53df009d1e1ba40f696f8995683e067e3967101d4bb4ea6f667931b7d4a01357"},
+    {file = "pydantic_core-2.10.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a1254357f7e4c82e77c348dabf2d55f1d14d19d91ff025004775e70a6ef40ada"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:524ff0ca3baea164d6d93a32c58ac79eca9f6cf713586fdc0adb66a8cdeab96a"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f0ac9fb8608dbc6eaf17956bf623c9119b4db7dbb511650910a82e261e6600f"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:320f14bd4542a04ab23747ff2c8a778bde727158b606e2661349557f0770711e"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:63974d168b6233b4ed6a0046296803cb13c56637a7b8106564ab575926572a55"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:417243bf599ba1f1fef2bb8c543ceb918676954734e2dcb82bf162ae9d7bd514"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:dda81e5ec82485155a19d9624cfcca9be88a405e2857354e5b089c2a982144b2"},
+    {file = "pydantic_core-2.10.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:14cfbb00959259e15d684505263d5a21732b31248a5dd4941f73a3be233865b9"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:631cb7415225954fdcc2a024119101946793e5923f6c4d73a5914d27eb3d3a05"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:bec7dd208a4182e99c5b6c501ce0b1f49de2802448d4056091f8e630b28e9a52"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:149b8a07712f45b332faee1a2258d8ef1fb4a36f88c0c17cb687f205c5dc6e7d"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4d966c47f9dd73c2d32a809d2be529112d509321c5310ebf54076812e6ecd884"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7eb037106f5c6b3b0b864ad226b0b7ab58157124161d48e4b30c4a43fef8bc4b"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:154ea7c52e32dce13065dbb20a4a6f0cc012b4f667ac90d648d36b12007fa9f7"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e562617a45b5a9da5be4abe72b971d4f00bf8555eb29bb91ec2ef2be348cd132"},
+    {file = "pydantic_core-2.10.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:f23b55eb5464468f9e0e9a9935ce3ed2a870608d5f534025cd5536bca25b1402"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:e9121b4009339b0f751955baf4543a0bfd6bc3f8188f8056b1a25a2d45099934"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:0523aeb76e03f753b58be33b26540880bac5aa54422e4462404c432230543f33"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e0e2959ef5d5b8dc9ef21e1a305a21a36e254e6a34432d00c72a92fdc5ecda5"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da01bec0a26befab4898ed83b362993c844b9a607a86add78604186297eb047e"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f2e9072d71c1f6cfc79a36d4484c82823c560e6f5599c43c1ca6b5cdbd54f881"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:f36a3489d9e28fe4b67be9992a23029c3cec0babc3bd9afb39f49844a8c721c5"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f64f82cc3443149292b32387086d02a6c7fb39b8781563e0ca7b8d7d9cf72bd7"},
+    {file = "pydantic_core-2.10.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b4a6db486ac8e99ae696e09efc8b2b9fea67b63c8f88ba7a1a16c24a057a0776"},
+    {file = "pydantic_core-2.10.1.tar.gz", hash = "sha256:0f8682dbdd2f67f8e1edddcbffcc29f60a6182b4901c367fc8c1c40d30bb0a82"},
 ]
 
 [package.dependencies]
@@ -1618,60 +1560,12 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.23"
+version = "2.0.22"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:638c2c0b6b4661a4fd264f6fb804eccd392745c5887f9317feb64bb7cb03b3ea"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e3b5036aa326dc2df50cba3c958e29b291a80f604b1afa4c8ce73e78e1c9f01d"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:787af80107fb691934a01889ca8f82a44adedbf5ef3d6ad7d0f0b9ac557e0c34"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c14eba45983d2f48f7546bb32b47937ee2cafae353646295f0e99f35b14286ab"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0666031df46b9badba9bed00092a1ffa3aa063a5e68fa244acd9f08070e936d3"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:89a01238fcb9a8af118eaad3ffcc5dedaacbd429dc6fdc43fe430d3a941ff965"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-win32.whl", hash = "sha256:cabafc7837b6cec61c0e1e5c6d14ef250b675fa9c3060ed8a7e38653bd732ff8"},
-    {file = "SQLAlchemy-2.0.23-cp310-cp310-win_amd64.whl", hash = "sha256:87a3d6b53c39cd173990de2f5f4b83431d534a74f0e2f88bd16eabb5667e65c6"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d5578e6863eeb998980c212a39106ea139bdc0b3f73291b96e27c929c90cd8e1"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:62d9e964870ea5ade4bc870ac4004c456efe75fb50404c03c5fd61f8bc669a72"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c80c38bd2ea35b97cbf7c21aeb129dcbebbf344ee01a7141016ab7b851464f8e"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75eefe09e98043cff2fb8af9796e20747ae870c903dc61d41b0c2e55128f958d"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:bd45a5b6c68357578263d74daab6ff9439517f87da63442d244f9f23df56138d"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:a86cb7063e2c9fb8e774f77fbf8475516d270a3e989da55fa05d08089d77f8c4"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-win32.whl", hash = "sha256:b41f5d65b54cdf4934ecede2f41b9c60c9f785620416e8e6c48349ab18643855"},
-    {file = "SQLAlchemy-2.0.23-cp311-cp311-win_amd64.whl", hash = "sha256:9ca922f305d67605668e93991aaf2c12239c78207bca3b891cd51a4515c72e22"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d0f7fb0c7527c41fa6fcae2be537ac137f636a41b4c5a4c58914541e2f436b45"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7c424983ab447dab126c39d3ce3be5bee95700783204a72549c3dceffe0fc8f4"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f508ba8f89e0a5ecdfd3761f82dda2a3d7b678a626967608f4273e0dba8f07ac"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6463aa765cf02b9247e38b35853923edbf2f6fd1963df88706bc1d02410a5577"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e599a51acf3cc4d31d1a0cf248d8f8d863b6386d2b6782c5074427ebb7803bda"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fd54601ef9cc455a0c61e5245f690c8a3ad67ddb03d3b91c361d076def0b4c60"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-win32.whl", hash = "sha256:42d0b0290a8fb0165ea2c2781ae66e95cca6e27a2fbe1016ff8db3112ac1e846"},
-    {file = "SQLAlchemy-2.0.23-cp312-cp312-win_amd64.whl", hash = "sha256:227135ef1e48165f37590b8bfc44ed7ff4c074bf04dc8d6f8e7f1c14a94aa6ca"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:14aebfe28b99f24f8a4c1346c48bc3d63705b1f919a24c27471136d2f219f02d"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e983fa42164577d073778d06d2cc5d020322425a509a08119bdcee70ad856bf"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e0dc9031baa46ad0dd5a269cb7a92a73284d1309228be1d5935dac8fb3cae24"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5f94aeb99f43729960638e7468d4688f6efccb837a858b34574e01143cf11f89"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:63bfc3acc970776036f6d1d0e65faa7473be9f3135d37a463c5eba5efcdb24c8"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-win32.whl", hash = "sha256:f48ed89dd11c3c586f45e9eec1e437b355b3b6f6884ea4a4c3111a3358fd0c18"},
-    {file = "SQLAlchemy-2.0.23-cp37-cp37m-win_amd64.whl", hash = "sha256:1e018aba8363adb0599e745af245306cb8c46b9ad0a6fc0a86745b6ff7d940fc"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:64ac935a90bc479fee77f9463f298943b0e60005fe5de2aa654d9cdef46c54df"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c4722f3bc3c1c2fcc3702dbe0016ba31148dd6efcd2a2fd33c1b4897c6a19693"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4af79c06825e2836de21439cb2a6ce22b2ca129bad74f359bddd173f39582bf5"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:683ef58ca8eea4747737a1c35c11372ffeb84578d3aab8f3e10b1d13d66f2bc4"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:d4041ad05b35f1f4da481f6b811b4af2f29e83af253bf37c3c4582b2c68934ab"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:aeb397de65a0a62f14c257f36a726945a7f7bb60253462e8602d9b97b5cbe204"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-win32.whl", hash = "sha256:42ede90148b73fe4ab4a089f3126b2cfae8cfefc955c8174d697bb46210c8306"},
-    {file = "SQLAlchemy-2.0.23-cp38-cp38-win_amd64.whl", hash = "sha256:964971b52daab357d2c0875825e36584d58f536e920f2968df8d581054eada4b"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:616fe7bcff0a05098f64b4478b78ec2dfa03225c23734d83d6c169eb41a93e55"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0e680527245895aba86afbd5bef6c316831c02aa988d1aad83c47ffe92655e74"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9585b646ffb048c0250acc7dad92536591ffe35dba624bb8fd9b471e25212a35"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4895a63e2c271ffc7a81ea424b94060f7b3b03b4ea0cd58ab5bb676ed02f4221"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:cc1d21576f958c42d9aec68eba5c1a7d715e5fc07825a629015fe8e3b0657fb0"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:967c0b71156f793e6662dd839da54f884631755275ed71f1539c95bbada9aaab"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-win32.whl", hash = "sha256:0a8c6aa506893e25a04233bc721c6b6cf844bafd7250535abb56cb6cc1368884"},
-    {file = "SQLAlchemy-2.0.23-cp39-cp39-win_amd64.whl", hash = "sha256:f3420d00d2cb42432c1d0e44540ae83185ccbbc67a6054dcc8ab5387add6620b"},
-    {file = "SQLAlchemy-2.0.23-py3-none-any.whl", hash = "sha256:31952bbc527d633b9479f5f81e8b9dfada00b91d6baba021a869095f1a97006d"},
-    {file = "SQLAlchemy-2.0.23.tar.gz", hash = "sha256:c1bda93cbbe4aa2aa0aa8655c5aeda505cd219ff3e8da91d1d329e143e4aff69"},
+    {file = "SQLAlchemy-2.0.22.tar.gz", hash = "sha256:5434cc601aa17570d79e5377f5fd45ff92f9379e2abed0be5e8c2fba8d353d2b"},
 ]
 
 [package.dependencies]
@@ -1680,8 +1574,7 @@ typing-extensions = ">=4.2.0"
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
-aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@@ -1691,7 +1584,7 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)"]
 mysql = ["mysqlclient (>=1.4.0)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx-oracle (>=8)"]
+oracle = ["cx_oracle (>=7)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
@@ -1701,7 +1594,7 @@ postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
 postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "sse-starlette"
@@ -1896,17 +1789,18 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "urllib3"
-version = "2.1.0"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
@@ -2033,4 +1927,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "0eefc6247307d0513ec0a820a3ee4f2b259cf411ea661a65927cb24ba6da4e08"
+content-hash = "078747db3e381f1480af6917fb388512c05682d97bfe4c650cb94a9bd3db4dc1"

templates/rag-timescale-conversation/pyproject.toml

@@ -9,7 +9,7 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain = ">=0.0.335"
+langchain = ">=0.0.325"
 openai = ">=0.28.1"
 tiktoken = ">=0.5.1"
 pinecone-client = ">=2.2.4"

templates/research-assistant/research_assistant/writer.py

@@ -1,13 +1,8 @@
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import ConfigurableField
 
-WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."  # noqa: E501
-
-
-# Report prompts from https://github.com/assafelovic/gpt-researcher/blob/master/gpt_researcher/master/prompts.py
-RESEARCH_REPORT_TEMPLATE = """Information: 
+REPORT_TEMPLATE = """Information: 
 --------
 {research_summary}
 --------
@@ -23,53 +18,14 @@ Write all used source urls at the end of the report, and make sure to not add du
 You must write the report in apa format.
 Please do your best, this is very important to my career."""  # noqa: E501
 
-
-RESOURCE_REPORT_TEMPLATE = """Information: 
---------
-{research_summary}
---------
-
-Based on the above information, generate a bibliography recommendation report for the following question or topic: "{question}". \
-The report should provide a detailed analysis of each recommended resource, explaining how each source can contribute to finding answers to the research question. \
-Focus on the relevance, reliability, and significance of each source. \
-Ensure that the report is well-structured, informative, in-depth, and follows Markdown syntax. \
-Include relevant facts, figures, and numbers whenever available. \
-The report should have a minimum length of 1,200 words.
-
-Please do your best, this is very important to my career."""  # noqa: E501
-
-OUTLINE_REPORT_TEMPLATE = """Information: 
---------
-{research_summary}
---------
-
-Using the above information, generate an outline for a research report in Markdown syntax for the following question or topic: "{question}". \
-The outline should provide a well-structured framework for the research report, including the main sections, subsections, and key points to be covered. \
-The research report should be detailed, informative, in-depth, and a minimum of 1,200 words. \
-Use appropriate Markdown syntax to format the outline and ensure readability.
-
-Please do your best, this is very important to my career."""  # noqa: E501
-
 model = ChatOpenAI(temperature=0)
 prompt = ChatPromptTemplate.from_messages(
     [
-        ("system", WRITER_SYSTEM_PROMPT),
-        ("user", RESEARCH_REPORT_TEMPLATE),
+        (
+            "system",
+            "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text.",  # noqa: E501
+        ),
+        ("user", REPORT_TEMPLATE),
     ]
-).configurable_alternatives(
-    ConfigurableField("report_type"),
-    default_key="research_report",
-    resource_report=ChatPromptTemplate.from_messages(
-        [
-            ("system", WRITER_SYSTEM_PROMPT),
-            ("user", RESOURCE_REPORT_TEMPLATE),
-        ]
-    ),
-    outline_report=ChatPromptTemplate.from_messages(
-        [
-            ("system", WRITER_SYSTEM_PROMPT),
-            ("user", OUTLINE_REPORT_TEMPLATE),
-        ]
-    ),
 )
 chain = prompt | model | StrOutputParser()