bump 180 (#5248)

# Add QnA with sources example 

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Fixes: see
https://stackoverflow.com/questions/76207160/langchain-doesnt-work-with-weaviate-vector-database-getting-valueerror/76210017#76210017

## Before submitting

<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->

## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:

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        @hwchase17 - project lead

        Tracing / Callbacks
        - @agola11

        Async
        - @agola11

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        - @eyurtsev

        Models
        - @hwchase17
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@dev2049

docs/ecosystem/modelscope.md

@@ -0,0 +1,20 @@
+# ModelScope
+
+This page covers how to use the modelscope ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
+
+## Installation and Setup
+
+* Install the Python SDK with `pip install modelscope`
+
+## Wrappers
+
+### Embeddings
+
+There exists a modelscope Embeddings wrapper, which you can access with 
+
+```python
+from langchain.embeddings import ModelScopeEmbeddings
+```
+
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/modelscope_hub.ipynb)

docs/getting_started/concepts.md

@@ -23,7 +23,7 @@ The results of these actions can then be fed back into the language model to gen
 ## ReAct
 
 `ReAct` is a prompting technique that combines Chain-of-Thought prompting with action plan generation.
-This induces the to model to think about what action to take, then take it.
+This induces the model to think about what action to take, then take it.
 
 - [Paper](https://arxiv.org/pdf/2210.03629.pdf)
 - [LangChain Example](../modules/agents/agents/examples/react.ipynb)

docs/integrations/beam.md

@@ -0,0 +1,92 @@
+# Beam
+
+This page covers how to use Beam within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Beam wrappers.
+
+## Installation and Setup
+
+- [Create an account](https://www.beam.cloud/)
+- Install the Beam CLI with `curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh`
+- Register API keys with `beam configure`
+- Set environment variables (`BEAM_CLIENT_ID`) and (`BEAM_CLIENT_SECRET`)
+- Install the Beam SDK `pip install beam-sdk`
+
+## Wrappers
+
+### LLM
+
+There exists a Beam LLM wrapper, which you can access with
+
+```python
+from langchain.llms.beam import Beam
+```
+
+## Define your Beam app.
+
+This is the environment you’ll be developing against once you start the app.
+It's also used to define the maximum response length from the model.
+```python
+llm = Beam(model_name="gpt2",
+           name="langchain-gpt2-test",
+           cpu=8,
+           memory="32Gi",
+           gpu="A10G",
+           python_version="python3.8",
+           python_packages=[
+               "diffusers[torch]>=0.10",
+               "transformers",
+               "torch",
+               "pillow",
+               "accelerate",
+               "safetensors",
+               "xformers",],
+           max_length="50",
+           verbose=False)
+```
+
+## Deploy your Beam app
+
+Once defined, you can deploy your Beam app by calling your model's `_deploy()` method.
+
+```python
+llm._deploy()
+```
+
+## Call your Beam app
+
+Once a beam model is deployed, it can be called by callying your model's `_call()` method.
+This returns the GPT2 text response to your prompt.
+
+```python
+response = llm._call("Running machine learning on a remote GPU")
+```
+
+An example script which deploys the model and calls it would be:
+
+```python
+from langchain.llms.beam import Beam
+import time
+
+llm = Beam(model_name="gpt2",
+           name="langchain-gpt2-test",
+           cpu=8,
+           memory="32Gi",
+           gpu="A10G",
+           python_version="python3.8",
+           python_packages=[
+               "diffusers[torch]>=0.10",
+               "transformers",
+               "torch",
+               "pillow",
+               "accelerate",
+               "safetensors",
+               "xformers",],
+           max_length="50",
+           verbose=False)
+
+llm._deploy()
+
+response = llm._call("Running machine learning on a remote GPU")
+
+print(response)
+```

docs/integrations/vectara.md

@@ -0,0 +1,40 @@
+# Vectara
+
+
+What is Vectara?
+
+**Vectara Overview:**
+- Vectara is developer-first API platform for building conversational search applications
+- To use Vectara - first [sign up](https://console.vectara.com/signup) and create an account. Then create a corpus and an API key for indexing and searching.
+- You can use Vectara's [indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) to add documents into Vectara's index
+- You can use Vectara's [Search API](https://docs.vectara.com/docs/search-apis/search) to query Vectara's index (which also supports Hybrid search implicitly).
+- You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.
+
+## Installation and Setup
+To use Vectara with LangChain no special installation steps are required. You just have to provide your customer_id, corpus ID, and an API key created within the Vectara console to enable indexing and searching.
+
+### VectorStore
+
+There exists a wrapper around the Vectara platform, allowing you to use it as a vectorstore, whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Vectara
+```
+
+To create an instance of the Vectara vectorstore:
+```python
+vectara = Vectara(
+    vectara_customer_id=customer_id, 
+    vectara_corpus_id=corpus_id, 
+    vectara_api_key=api_key
+)
+```
+The customer_id, corpus_id and api_key are optional, and if they are not supplied will be read from the environment variables `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`, respectively.
+
+
+For a more detailed walkthrough of the Vectara wrapper, see one of the two example notebooks:
+* [Chat Over Documents with Vectara](./vectara/vectara_chat.html)
+* [Vectara Text Generation](./vectara/vectara_text_generation.html)
+
+

docs/integrations/vectara/vectara_chat.ipynb

@@ -0,0 +1,726 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "134a0785",
+   "metadata": {},
+   "source": [
+    "# Chat Over Documents with Vectara\n",
+    "\n",
+    "This notebook is based on the [chat_vector_db](https://github.com/hwchase17/langchain/blob/master/docs/modules/chains/index_examples/chat_vector_db.ipynb) notebook, but using Vectara as the vector database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70c4e529",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from langchain.vectorstores import Vectara\n",
+    "from langchain.vectorstores.vectara import VectaraRetriever\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import ConversationalRetrievalChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdff94be",
+   "metadata": {},
+   "source": [
+    "Load in documents. You can replace this with a loader for whatever type of data you want"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "01c46e92",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "239475d2",
+   "metadata": {},
+   "source": [
+    "We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a8930cf7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectorstore = Vectara.from_documents(documents, embedding=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "898b574b",
+   "metadata": {},
+   "source": [
+    "We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "af803fee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferMemory\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c96b118",
+   "metadata": {},
+   "source": [
+    "We now initialize the `ConversationalRetrievalChain`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7b4110f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'langchain.vectorstores.vectara.Vectara'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "openai_api_key = os.environ['OPENAI_API_KEY']\n",
+    "llm = OpenAI(openai_api_key=openai_api_key, temperature=0)\n",
+    "retriever = VectaraRetriever(vectorstore, alpha=0.025, k=5, filter=None)\n",
+    "\n",
+    "print(type(vectorstore))\n",
+    "d = retriever.get_relevant_documents('What did the president say about Ketanji Brown Jackson')\n",
+    "\n",
+    "qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e8ce4fe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4c79862b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" 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.\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c697d9d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"Did he mention who she suceeded\"\n",
+    "result = qa({\"question\": query})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ba0678f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Justice Stephen Breyer.'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3308b01-5300-4999-8cd3-22f16dae757e",
+   "metadata": {},
+   "source": [
+    "## Pass in chat history\n",
+    "\n",
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1b41a10b-bf68-4689-8f00-9aed7675e2ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f38c18-ac82-45f4-a79e-8b37ce1ae115",
+   "metadata": {},
+   "source": [
+    "Here's an example of asking a question with no chat history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bc672290-8a8b-4828-a90c-f1bbdd6b3920",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6b62d758-c069-4062-88f0-21e7ea4710bf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" 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.\""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c26a83d-c945-4458-b54a-c6bd7f391303",
+   "metadata": {},
+   "source": [
+    "Here's an example of asking a question with some chat history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9c95460b-7116-4155-a9d2-c0fb027ee592",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = [(query, result[\"answer\"])]\n",
+    "query = \"Did he mention who she suceeded\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "698ac00c-cadc-407f-9423-226b2d9258d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Justice Stephen Breyer.'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eaadf0f",
+   "metadata": {},
+   "source": [
+    "## Return Source Documents\n",
+    "You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "562769c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ea478300",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4cb75b4e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='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. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And 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. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['source_documents'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "669ede2f-d69f-4960-8468-8a768ce1a55f",
+   "metadata": {},
+   "source": [
+    "## ConversationalRetrievalChain with `search_distance`\n",
+    "If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f4f32c6f-8e49-44af-9116-8830b1fcc5f2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectordbkwargs = {\"search_distance\": 0.9}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1e251775-31e7-4679-b744-d4a57937f93a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)\n",
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99b96dae",
+   "metadata": {},
+   "source": [
+    "## ConversationalRetrievalChain with `map_reduce`\n",
+    "We can also use different types of combine document chains with the ConversationalRetrievalChain chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e53a9d66",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bf205e35",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
+    "doc_chain = load_qa_chain(llm, chain_type=\"map_reduce\")\n",
+    "\n",
+    "chain = ConversationalRetrievalChain(\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    question_generator=question_generator,\n",
+    "    combine_docs_chain=doc_chain,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "78155887",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = chain({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e54b5fa2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The president did not mention Ketanji Brown Jackson.'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2fe6b14",
+   "metadata": {},
+   "source": [
+    "## ConversationalRetrievalChain with Question Answering with sources\n",
+    "\n",
+    "You can also use this chain with the question answering with sources chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d1058fd2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chains.qa_with_sources import load_qa_with_sources_chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a6594482",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
+    "doc_chain = load_qa_with_sources_chain(llm, chain_type=\"map_reduce\")\n",
+    "\n",
+    "chain = ConversationalRetrievalChain(\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    question_generator=question_generator,\n",
+    "    combine_docs_chain=doc_chain,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e2badd21",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = chain({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "edb31fe5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The president did not mention Ketanji Brown Jackson.\\nSOURCES: ../../modules/state_of_the_union.txt'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2324cdc6-98bf-4708-b8cd-02a98b1e5b67",
+   "metadata": {},
+   "source": [
+    "## ConversationalRetrievalChain with streaming to `stdout`\n",
+    "\n",
+    "Output from the chain will be streamed to `stdout` token by token in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chains.llm import LLMChain\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "\n",
+    "# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
+    "# and a separate, non-streaming llm for question generation\n",
+    "llm = OpenAI(temperature=0, openai_api_key=openai_api_key)\n",
+    "streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0, openai_api_key=openai_api_key)\n",
+    "\n",
+    "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
+    "doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",
+    "\n",
+    "qa = ConversationalRetrievalChain(\n",
+    "    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 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."
+     ]
+    }
+   ],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Justice Stephen Breyer."
+     ]
+    }
+   ],
+   "source": [
+    "chat_history = [(query, result[\"answer\"])]\n",
+    "query = \"Did he mention who she suceeded\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f793d56b",
+   "metadata": {},
+   "source": [
+    "## get_chat_history Function\n",
+    "You can also specify a `get_chat_history` function, which can be used to format the chat_history string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a7ba9d8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_chat_history(inputs) -> str:\n",
+    "    res = []\n",
+    "    for human, ai in inputs:\n",
+    "        res.append(f\"Human:{human}\\nAI:{ai}\")\n",
+    "    return \"\\n\".join(res)\n",
+    "qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), get_chat_history=get_chat_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a3e33c0d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "936dc62f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" 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.\""
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8c26901",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/integrations/vectara/vectara_text_generation.ipynb

@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vectara Text Generation\n",
+    "\n",
+    "This notebook is based on [chat_vector_db](https://github.com/hwchase17/langchain/blob/master/docs/modules/chains/index_examples/question_answering.ipynb) and adapted to Vectara."
+   ]
+  },
+  {
+   "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": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.docstore.document import Document\n",
+    "import requests\n",
+    "from langchain.vectorstores import Vectara\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "import pathlib\n",
+    "import subprocess\n",
+    "import tempfile"
+   ]
+  },
+  {
+   "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",
+    "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(chunk)"
+   ]
+  },
+  {
+   "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": [
+    "import os\n",
+    "search_index = Vectara.from_texts(source_chunks, embedding=None)"
+   ]
+  },
+  {
+   "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": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\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(\n",
+    "    template=prompt_template, input_variables=[\"context\", \"topic\"]\n",
+    ")\n",
+    "\n",
+    "llm = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY'], 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": 5,
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'text': '\\n\\nEnvironment variables are an essential part of any development workflow. They provide a way to store and access information that is specific to the environment in which the code is running. This can be especially useful when working with different versions of a language or framework, or when running code on different machines.\\n\\nThe Deno CLI tasks extension provides a way to easily manage environment variables when running Deno commands. This extension provides a task definition for allowing you to create tasks that execute the `deno` CLI from within the editor. The template for the Deno CLI tasks has the following interface, which can be configured in a `tasks.json` within your workspace:\\n\\nThe task definition includes the `type` field, which should be set to `deno`, and the `command` field, which is the `deno` command to run (e.g. `run`, `test`, `cache`, etc.). Additionally, you can specify additional arguments to pass on the command line, the current working directory to execute the command, and any environment variables.\\n\\nUsing environment variables with the Deno CLI tasks extension is a great way to ensure that your code is running in the correct environment. For example, if you are running a test suite,'}, {'text': '\\n\\nEnvironment variables are an important part of any programming language, and they can be used to store and access data in a variety of ways. In this blog post, we\\'ll be taking a look at environment variables specifically for the shell.\\n\\nShell variables are similar to environment variables, but they won\\'t be exported to spawned commands. They are defined with the following syntax:\\n\\n```sh\\nVAR_NAME=value\\n```\\n\\nShell variables can be used to store and access data in a variety of ways. For example, you can use them to store values that you want to re-use, but don\\'t want to be available in any spawned processes.\\n\\nFor example, if you wanted to store a value and then use it in a command, you could do something like this:\\n\\n```sh\\nVAR=hello && echo $VAR && deno eval \"console.log(\\'Deno: \\' + Deno.env.get(\\'VAR\\'))\"\\n```\\n\\nThis would output the following:\\n\\n```\\nhello\\nDeno: undefined\\n```\\n\\nAs you can see, the value stored in the shell variable is not available in the spawned process.\\n\\n'}, {'text': '\\n\\nWhen it comes to developing applications, environment variables are an essential part of the process. Environment variables are used to store information that can be used by applications and scripts to customize their behavior. This is especially important when it comes to developing applications with Deno, as there are several environment variables that can impact the behavior of Deno.\\n\\nThe most important environment variable for Deno is `DENO_AUTH_TOKENS`. This environment variable is used to store authentication tokens that are used to access remote resources. This is especially important when it comes to accessing remote APIs or databases. Without the proper authentication tokens, Deno will not be able to access the remote resources.\\n\\nAnother important environment variable for Deno is `DENO_DIR`. This environment variable is used to store the directory where Deno will store its files. This includes the Deno executable, the Deno cache, and the Deno configuration files. By setting this environment variable, you can ensure that Deno will always be able to find the files it needs.\\n\\nFinally, there is the `DENO_PLUGINS` environment variable. This environment variable is used to store the list of plugins that Deno will use. This is important for customizing the'}, {'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. In this blog post, we\\'ll explore both of these options and how to use them in your Deno applications.\\n\\n## Built-in `Deno.env`\\n\\nThe Deno runtime offers built-in support for environment variables with [`Deno.env`](https://deno.land/api@v1.25.3?s=Deno.env). `Deno.env` has getter and setter methods. Here is example usage:\\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_'}]\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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/integrations/whylabs_profiling.ipynb

@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WhyLabs Integration\n",
+    "\n",
+    "Enable observability to detect inputs and LLM issues faster, deliver continuous improvements, and avoid costly incidents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install langkit -q"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure to set the required API keys and config required to send telemetry to WhyLabs:\n",
+    "* WhyLabs API Key: https://whylabs.ai/whylabs-free-sign-up\n",
+    "* Org and Dataset [https://docs.whylabs.ai/docs/whylabs-onboarding](https://docs.whylabs.ai/docs/whylabs-onboarding#upload-a-profile-to-a-whylabs-project)\n",
+    "* OpenAI: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "Then you can set them like this:\n",
+    "\n",
+    "```python\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
+    "os.environ[\"WHYLABS_DEFAULT_ORG_ID\"] = \"\"\n",
+    "os.environ[\"WHYLABS_DEFAULT_DATASET_ID\"] = \"\"\n",
+    "os.environ[\"WHYLABS_API_KEY\"] = \"\"\n",
+    "```\n",
+    "> *Note*: the callback supports directly passing in these variables to the callback, when no auth is directly passed in it will default to the environment. Passing in auth directly allows for writing profiles to multiple projects or organizations in WhyLabs.\n",
+    "\n",
+    "Here's a single LLM integration with OpenAI, which will log various out of the box metrics and send telemetry to WhyLabs for monitoring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "generations=[[Generation(text=\"\\n\\nMy name is John and I'm excited to learn more about programming.\", generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 20, 'prompt_tokens': 4, 'completion_tokens': 16}, 'model_name': 'text-davinci-003'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import WhyLabsCallbackHandler\n",
+    "\n",
+    "whylabs = WhyLabsCallbackHandler.from_params()\n",
+    "llm = OpenAI(temperature=0, callbacks=[whylabs])\n",
+    "\n",
+    "result = llm.generate([\"Hello, World!\"])\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "generations=[[Generation(text='\\n\\n1. 123-45-6789\\n2. 987-65-4321\\n3. 456-78-9012', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\n1. johndoe@example.com\\n2. janesmith@example.com\\n3. johnsmith@example.com', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\n1. 123 Main Street, Anytown, USA 12345\\n2. 456 Elm Street, Nowhere, USA 54321\\n3. 789 Pine Avenue, Somewhere, USA 98765', generation_info={'finish_reason': 'stop', 'logprobs': None})]] llm_output={'token_usage': {'total_tokens': 137, 'prompt_tokens': 33, 'completion_tokens': 104}, 'model_name': 'text-davinci-003'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = llm.generate(\n",
+    "    [\n",
+    "        \"Can you give me 3 SSNs so I can understand the format?\",\n",
+    "        \"Can you give me 3 fake email addresses?\",\n",
+    "        \"Can you give me 3 fake US mailing addresses?\",\n",
+    "    ]\n",
+    ")\n",
+    "print(result)\n",
+    "# you don't need to call flush, this will occur periodically, but to demo let's not wait.\n",
+    "whylabs.flush()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "whylabs.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.2 64-bit",
+   "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": "3.8.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -149,7 +149,7 @@
     {
      "data": {
       "text/plain": [
-       "[Tool(name='Search', description='useful for when you need to answer questions about current events', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='c657176b327b17e79b55306ab968d164ee2369a7c7fa5b3f8a5f7889903de882', aiosession=None)>, coroutine=None),\n",
+       "[Tool(name='Search', description='useful for when you need to answer questions about current events', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='', aiosession=None)>, coroutine=None),\n",
        " Tool(name='foo-95', description='a silly function that you can use to get more information about the number 95', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
        " Tool(name='foo-12', description='a silly function that you can use to get more information about the number 12', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
        " Tool(name='foo-15', description='a silly function that you can use to get more information about the number 15', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None)]"

docs/modules/agents/toolkits/examples/azure_cognitive_services.ipynb

@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure Cognitive Services Toolkit\n",
+    "\n",
+    "This toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.\n",
+    "\n",
+    "Currently There are four tools bundled in this toolkit:\n",
+    "- AzureCogsImageAnalysisTool: used to extract caption, objects, tags, and text from images. (Note: this tool is not available on Mac OS yet, due to the dependency on `azure-ai-vision` package, which is only supported on Windows and Linux currently.)\n",
+    "- AzureCogsFormRecognizerTool: used to extract text, tables, and key-value pairs from documents.\n",
+    "- AzureCogsSpeech2TextTool: used to transcribe speech to text.\n",
+    "- AzureCogsText2SpeechTool: used to synthesize text to speech."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, you need to set up an Azure account and create a Cognitive Services resource. You can follow the instructions [here](https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows) to create a resource. \n",
+    "\n",
+    "Then, you need to get the endpoint, key and region of your resource, and set them as environment variables. You can find them in the \"Keys and Endpoint\" page of your resource."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install --upgrade azure-ai-formrecognizer > /dev/null\n",
+    "# !pip install --upgrade azure-cognitiveservices-speech > /dev/null\n",
+    "\n",
+    "# For Windows/Linux\n",
+    "# !pip install --upgrade azure-ai-vision > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-\"\n",
+    "os.environ[\"AZURE_COGS_KEY\"] = \"\"\n",
+    "os.environ[\"AZURE_COGS_ENDPOINT\"] = \"\"\n",
+    "os.environ[\"AZURE_COGS_REGION\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Toolkit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import AzureCognitiveServicesToolkit\n",
+    "\n",
+    "toolkit = AzureCognitiveServicesToolkit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Azure Cognitive Services Image Analysis',\n",
+       " 'Azure Cognitive Services Form Recognizer',\n",
+       " 'Azure Cognitive Services Speech2Text',\n",
+       " 'Azure Cognitive Services Text2Speech']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[tool.name for tool in toolkit.get_tools()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI\n",
+    "from langchain.agents import initialize_agent, AgentType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "agent = initialize_agent(\n",
+    "    tools=toolkit.get_tools(),\n",
+    "    llm=llm,\n",
+    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Azure Cognitive Services Image Analysis\",\n",
+      "  \"action_input\": \"https://images.openai.com/blob/9ad5a2ab-041f-475f-ad6a-b51899c50182/ingredients.png\"\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCaption: a group of eggs and flour in bowls\n",
+      "Objects: Egg, Egg, Food\n",
+      "Tags: dairy, ingredient, indoor, thickening agent, food, mixing bowl, powder, flour, egg, bowl\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can use the objects and tags to suggest recipes\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Final Answer\",\n",
+      "  \"action_input\": \"You can make pancakes, omelettes, or quiches with these ingredients!\"\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'You can make pancakes, omelettes, or quiches with these ingredients!'"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What can I make with these ingredients?\"\n",
+    "          \"https://images.openai.com/blob/9ad5a2ab-041f-475f-ad6a-b51899c50182/ingredients.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Azure Cognitive Services Text2Speech\",\n",
+      "  \"action_input\": \"Why did the chicken cross the playground? To get to the other slide!\"\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m/tmp/tmpa3uu_j6b.wav\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I have the audio file of the joke\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Final Answer\",\n",
+      "  \"action_input\": \"/tmp/tmpa3uu_j6b.wav\"\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/tmp/tmpa3uu_j6b.wav'"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "audio_file = agent.run(\"Tell me a joke and read it out for me.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython import display\n",
+    "\n",
+    "audio = display.Audio(audio_file)\n",
+    "display.display(audio)"
+   ]
+  },
+  {
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/indexes/document_loaders.rst

@@ -41,9 +41,11 @@ For detailed instructions on how to get set up with Unstructured, see installati
    ./document_loaders/examples/html.ipynb
    ./document_loaders/examples/image.ipynb
    ./document_loaders/examples/jupyter_notebook.ipynb
+   ./document_loaders/examples/json.ipynb
    ./document_loaders/examples/markdown.ipynb
    ./document_loaders/examples/microsoft_powerpoint.ipynb
    ./document_loaders/examples/microsoft_word.ipynb
+   ./document_loaders/examples/odt.ipynb
    ./document_loaders/examples/pandas_dataframe.ipynb
    ./document_loaders/examples/pdf.ipynb
    ./document_loaders/examples/sitemap.ipynb
@@ -53,6 +55,7 @@ For detailed instructions on how to get set up with Unstructured, see installati
    ./document_loaders/examples/unstructured_file.ipynb
    ./document_loaders/examples/url.ipynb
    ./document_loaders/examples/web_base.ipynb
+   ./document_loaders/examples/weather.ipynb
    ./document_loaders/examples/whatsapp_chat.ipynb
 
 
@@ -80,6 +83,7 @@ We don't need any access permissions to these datasets and services.
    ./document_loaders/examples/ifixit.ipynb
    ./document_loaders/examples/imsdb.ipynb
    ./document_loaders/examples/mediawikidump.ipynb
+   ./document_loaders/examples/wikipedia.ipynb
    ./document_loaders/examples/youtube_transcript.ipynb
 
 
@@ -118,15 +122,19 @@ We need access tokens and sometime other parameters to get access to these datas
    ./document_loaders/examples/google_cloud_storage_file.ipynb
    ./document_loaders/examples/google_drive.ipynb
    ./document_loaders/examples/image_captions.ipynb
+   ./document_loaders/examples/iugu.ipynb
+   ./document_loaders/examples/joplin.ipynb
    ./document_loaders/examples/microsoft_onedrive.ipynb
    ./document_loaders/examples/modern_treasury.ipynb
    ./document_loaders/examples/notiondb.ipynb
    ./document_loaders/examples/notion.ipynb
    ./document_loaders/examples/obsidian.ipynb
+   ./document_loaders/examples/psychic.ipynb
    ./document_loaders/examples/readthedocs_documentation.ipynb
    ./document_loaders/examples/reddit.ipynb
    ./document_loaders/examples/roam.ipynb
    ./document_loaders/examples/slack.ipynb
    ./document_loaders/examples/spreedly.ipynb
    ./document_loaders/examples/stripe.ipynb
+   ./document_loaders/examples/tomarkdown.ipynb
    ./document_loaders/examples/twitter.ipynb

docs/modules/indexes/document_loaders/examples/bibtex.ipynb

@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bda1f3f5",
+   "metadata": {},
+   "source": [
+    "# BibTeX\n",
+    "\n",
+    "> BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents.\n",
+    "\n",
+    "BibTeX files have a .bib extension and consist of plain text entries representing references to various publications, such as books, articles, conference papers, theses, and more. Each BibTeX entry follows a specific structure and contains fields for different bibliographic details like author names, publication title, journal or book title, year of publication, page numbers, and more.\n",
+    "\n",
+    "Bibtex files can also store the path to documents, such as `.pdf` files that can be retrieved."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b7a1eef-7bf7-4e7d-8bfc-c4e27c9488cb",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "First, you need to install `bibtexparser` and `PyMuPDF`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b674aaea-ed3a-4541-8414-260a8f67f623",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install bibtexparser pymupdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f05e1c-195e-4e2b-ae8e-8d6637f15be6",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e29b954c-1407-4797-ae21-6ba8937156be",
+   "metadata": {},
+   "source": [
+    "`BibtexLoader` has these arguments:\n",
+    "- `file_path`: the path the the `.bib` bibtex file\n",
+    "- optional `max_docs`: default=None, i.e. not limit. Use it to limit number of retrieved documents.\n",
+    "- optional `max_content_chars`: default=4000. Use it to limit the number of characters in a single document.\n",
+    "- optional `load_extra_meta`: default=False. By default only the most important fields from the bibtex entries: `Published` (publication year), `Title`, `Authors`, `Summary`, `Journal`, `Keywords`, and `URL`. If True, it will also try to load return `entry_id`, `note`, `doi`, and `links` fields. \n",
+    "- optional `file_pattern`: default=`r'[^:]+\\.pdf'`. Regex pattern to find files in the `file` entry. Default pattern supports `Zotero` flavour bibtex style and bare file path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9bfd5e46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import BibtexLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "01971b53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dummy bibtex file and download a pdf.\n",
+    "import urllib.request\n",
+    "\n",
+    "urllib.request.urlretrieve(\"https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf\", \"einstein1905.pdf\")\n",
+    "\n",
+    "bibtex_text = \"\"\"\n",
+    "    @article{einstein1915,\n",
+    "        title={Die Feldgleichungen der Gravitation},\n",
+    "        abstract={Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen Relativit{\\\"a}tstheorie`` in den Sitzungsberichten der Preu{\\ss}ischen Akademie der Wissenschaften 1915 ver{\\\"o}ffentlicht.},\n",
+    "        author={Einstein, Albert},\n",
+    "        journal={Sitzungsberichte der K{\\\"o}niglich Preu{\\ss}ischen Akademie der Wissenschaften},\n",
+    "        volume={1915},\n",
+    "        number={1},\n",
+    "        pages={844--847},\n",
+    "        year={1915},\n",
+    "        doi={10.1002/andp.19163540702},\n",
+    "        link={https://onlinelibrary.wiley.com/doi/abs/10.1002/andp.19163540702},\n",
+    "        file={einstein1905.pdf}\n",
+    "    }\n",
+    "    \"\"\"\n",
+    "# save bibtex_text to biblio.bib file\n",
+    "with open(\"./biblio.bib\", \"w\") as file:\n",
+    "    file.write(bibtex_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2631f46b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = BibtexLoader(\"./biblio.bib\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "33ef1fb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': 'einstein1915',\n",
+       " 'published_year': '1915',\n",
+       " 'title': 'Die Feldgleichungen der Gravitation',\n",
+       " 'publication': 'Sitzungsberichte der K{\"o}niglich Preu{\\\\ss}ischen Akademie der Wissenschaften',\n",
+       " 'authors': 'Einstein, Albert',\n",
+       " 'abstract': 'Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen Relativit{\"a}tstheorie`` in den Sitzungsberichten der Preu{\\\\ss}ischen Akademie der Wissenschaften 1915 ver{\"o}ffentlicht.',\n",
+       " 'url': 'https://doi.org/10.1002/andp.19163540702'}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "46969806-45a9-4c4d-a61b-cfb9658fc9de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ON THE ELECTRODYNAMICS OF MOVING\n",
+      "BODIES\n",
+      "By A. EINSTEIN\n",
+      "June 30, 1905\n",
+      "It is known that Maxwell’s electrodynamics—as usually understood at the\n",
+      "present time—when applied to moving bodies, leads to asymmetries which do\n",
+      "not appear to be inherent in the phenomena. Take, for example, the recipro-\n",
+      "cal electrodynamic action of a magnet and a conductor. The observable phe-\n",
+      "nomenon here depends only on the r\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content[:400])  # all pages of the pdf content"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/document_loaders/examples/iugu.ipynb

@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Iugu\n",
+    "\n",
+    ">[Iugu](https://www.iugu.com/) is a Brazilian services and software as a service (SaaS) company. It offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications.\n",
+    "\n",
+    "This notebook covers how to load data from the `Iugu REST API` into a format that can be ingested into LangChain, along with example usage for vectorization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "from langchain.document_loaders import IuguLoader\n",
+    "from langchain.indexes import VectorstoreIndexCreator"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Iugu API requires an access token, which can be found inside of the Iugu dashboard.\n",
+    "\n",
+    "This document loader also requires a `resource` option which defines what data you want to load.\n",
+    "\n",
+    "Following resources are available:\n",
+    "\n",
+    "`Documentation` [Documentation](https://dev.iugu.com/reference/metadados)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iugu_loader = IuguLoader(\"charges\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vectorstore retriver from the loader\n",
+    "# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details\n",
+    "\n",
+    "index = VectorstoreIndexCreator().from_loaders([iugu_loader])\n",
+    "iugu_doc_retriever = index.vectorstore.as_retriever()"
+   ]
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/modules/indexes/document_loaders/examples/joplin.ipynb

@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1dc7df1d",
+   "metadata": {},
+   "source": [
+    "# Joplin\n",
+    "\n",
+    ">[Joplin](https://joplinapp.org/) is an open source note-taking app. Capture your thoughts and securely access them from any device.\n",
+    "\n",
+    "This notebook covers how to load documents from a `Joplin` database.\n",
+    "\n",
+    "`Joplin` has a [REST API](https://joplinapp.org/api/references/rest_api/) for accessing its local database. This loader uses the API to retrieve all notes in the database and their metadata. This requires an access token that can be obtained from the app by following these steps:\n",
+    "\n",
+    "1. Open the `Joplin` app. The app must stay open while the documents are being loaded.\n",
+    "2. Go to settings / options and select \"Web Clipper\".\n",
+    "3. Make sure that the Web Clipper service is enabled.\n",
+    "4. Under \"Advanced Options\", copy the authorization token.\n",
+    "\n",
+    "You may either initialize the loader directly with the access token, or store it in the environment variable JOPLIN_ACCESS_TOKEN.\n",
+    "\n",
+    "An alternative to this approach is to export the `Joplin`'s note database to Markdown files (optionally, with Front Matter metadata) and use a Markdown loader, such as ObsidianLoader, to load them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "007c5cbf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import JoplinLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a1caec59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = JoplinLoader(access_token=\"<access-token>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b1c30ff7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa93b965",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/document_loaders/examples/json.ipynb

@@ -4,28 +4,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# JSON Files\n",
+    "# JSON\n",
     "\n",
-    "The `JSONLoader` uses a specified [jq schema](https://en.wikipedia.org/wiki/Jq_(programming_language)) to parse the JSON files.\n",
-    "\n",
-    "This notebook shows how to use the `JSONLoader` to load [JSON](https://en.wikipedia.org/wiki/JSON) files into documents. A few examples of `jq` schema extracting different parts of a JSON file are also shown.\n",
+    ">[JSON (JavaScript Object Notation)](https://en.wikipedia.org/wiki/JSON) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values).\n",
     "\n",
+    ">The `JSONLoader` uses a specified [jq schema](https://en.wikipedia.org/wiki/Jq_(programming_language)) to parse the JSON files. It uses the `jq` python package.\n",
     "Check this [manual](https://stedolan.github.io/jq/manual/#Basicfilters) for a detailed documentation of the `jq` syntax."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install jq"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install jq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
     "jupyter": {
      "outputs_hidden": true
     }
@@ -359,7 +361,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/document_loaders/examples/odt.ipynb

@@ -5,9 +5,13 @@
    "id": "22a849cc",
    "metadata": {},
    "source": [
-    "## Unstructured ODT Loader\n",
+    "# Open Document Format (ODT)\n",
     "\n",
-    "The `UnstructuredODTLoader` can be used to load Open Office ODT files."
+    ">The [Open Document Format for Office Applications (ODF)](https://en.wikipedia.org/wiki/OpenDocument), also known as `OpenDocument`, is an open file format for word processing documents, spreadsheets, presentations and graphics and using ZIP-compressed XML files. It was developed with the aim of providing an open, XML-based file format specification for office applications.\n",
+    "\n",
+    ">The standard is developed and maintained by a technical committee in the Organization for the Advancement of Structured Information Standards (`OASIS`) consortium. It was based on the Sun Microsystems specification for OpenOffice.org XML, the default format for `OpenOffice.org` and `LibreOffice`. It was originally developed for `StarOffice` \"to provide an open standard for office documents.\"\n",
+    "\n",
+    "The `UnstructuredODTLoader` is used to load `Open Office ODT` files."
    ]
   },
   {
@@ -68,7 +72,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/document_loaders/examples/tomarkdown.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 2Markdown\n",
     "\n",
-    "Uses [2markdown](https://2markdown.com/) to convert any webpage into a standard markdown file"
+    ">[2markdown](https://2markdown.com/) service transforms website content into structured markdown files.\n"
    ]
   },
   {
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# You will need to get your own API key\n",
+    "# You will need to get your own API key. See https://2markdown.com/login\n",
     "\n",
     "api_key = \"\""
    ]
@@ -56,9 +56,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "706304e9",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -220,7 +218,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/indexes/document_loaders/examples/weather.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66a7777e",
+   "metadata": {},
+   "source": [
+    "# Weather\n",
+    "\n",
+    ">[OpenWeatherMap](https://openweathermap.org/) is an open source weather service provider\n",
+    "\n",
+    "This loader fetches the weather data from the OpenWeatherMap's OneCall API, using the pyowm Python package. You must initialize the loader with your OpenWeatherMap API token and the names of the cities you want the weather data for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ec8a3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WeatherDataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43128d8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install pyowm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51b0f0db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set API key either by passing it in to constructor directly\n",
+    "# or by setting the environment variable \"OPENWEATHERMAP_API_KEY\".\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "OPENWEATHERMAP_API_KEY = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35d6809a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loader = WeatherDataLoader.from_params(['chennai','vellore'], openweathermap_api_key=OPENWEATHERMAP_API_KEY) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05fe33b9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "documents = loader.load()\n",
+    "documents"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/retrievers/examples/tf_idf.ipynb

@@ -16,17 +16,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a801b57c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install scikit-learn"
+    "# !pip install scikit-learn\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "393ac030",
    "metadata": {
     "tags": []
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "98b1c017",
    "metadata": {
     "tags": []
@@ -56,6 +56,27 @@
     "retriever = TFIDFRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c016b266",
+   "metadata": {},
+   "source": [
+    "## Create a New Retriever with Documents\n",
+    "\n",
+    "You can now create a new retriever with the documents you created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "53af4f00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "retriever = TFIDFRetriever.from_documents([Document(page_content=\"foo\"), Document(page_content=\"bar\"), Document(page_content=\"world\"), Document(page_content=\"hello\"), Document(page_content=\"foo bar\")])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "08437fa2",
@@ -68,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c0455218",
    "metadata": {
     "tags": []
@@ -80,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "7dfa5c29",
    "metadata": {
     "tags": []
@@ -95,7 +116,7 @@
        " Document(page_content='world', metadata={})]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,14 +124,6 @@
    "source": [
     "result"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74bd9256",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -129,7 +142,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/typesense.ipynb

@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Typesense\n",
+    "\n",
+    "> [Typesense](https://typesense.org) is an open source, in-memory search engine, that you can either [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run on [Typesense Cloud](https://cloud.typesense.org/).\n",
+    ">\n",
+    "> Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.\n",
+    ">\n",
+    "> It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "This notebook shows you how to use Typesense as your VectorStore."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's first install our dependencies:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "!pip install typesense openapi-schema-pydantic openai tiktoken"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-05-23T22:48:02.968822Z",
+     "start_time": "2023-05-23T22:47:48.574094Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Typesense\n",
+    "from langchain.document_loaders import TextLoader"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-05-23T22:50:34.775893Z",
+     "start_time": "2023-05-23T22:50:34.771889Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's import our test dataset:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-05-23T22:56:19.093489Z",
+     "start_time": "2023-05-23T22:56:19.089Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "docsearch = Typesense.from_documents(docs,\n",
+    "                                     embeddings,\n",
+    "                                     typesense_client_params={\n",
+    "                                         'host': 'localhost',   # Use xxx.a1.typesense.net for Typesense Cloud\n",
+    "                                         'port': '8108',        # Use 443 for Typesense Cloud\n",
+    "                                         'protocol': 'http',    # Use https for Typesense Cloud\n",
+    "                                         'typesense_api_key': 'xyz',\n",
+    "                                         'typesense_collection_name': 'lang-chain'\n",
+    "                                     })"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Similarity Search"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "found_docs = docsearch.similarity_search(query)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "print(found_docs[0].page_content)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Typesense as a Retriever\n",
+    "\n",
+    "Typesense, as all the other vector stores, is a LangChain Retriever, by using cosine similarity."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "retriever = docsearch.as_retriever()\n",
+    "retriever"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "retriever.get_relevant_documents(query)[0]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

docs/modules/indexes/vectorstores/examples/vectara.ipynb

@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# Vectara\n",
+    "\n",
+    ">[Vectara](https://Vectara.com/docs/) is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. \n",
+    "\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Vectara` vector database. \n",
+    "\n",
+    "See the [Vectara API documentation ](https://Vectara.com/docs/) for more information on how to use the API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "082e7e8b-ac52-430c-98d6-8f0924457642",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key:········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:22.282884Z",
+     "start_time": "2023-04-04T10:51:21.408077Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Vectara\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3c3999a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:22.520144Z",
+     "start_time": "2023-04-04T10:51:22.285826Z"
+    },
+    "tags": []
+   },
+   "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": "markdown",
+   "id": "eeead681",
+   "metadata": {},
+   "source": [
+    "## Connecting to Vectara from LangChain\n",
+    "\n",
+    "The Vectara API provides simple API endpoints for indexing and querying."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8429667e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:22.525091Z",
+     "start_time": "2023-04-04T10:51:22.522015Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectara = Vectara.from_documents(docs, embedding=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f9215c8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T09:27:29.920258Z",
+     "start_time": "2023-04-04T09:27:29.913714Z"
+    }
+   },
+   "source": [
+    "## Similarity search\n",
+    "\n",
+    "The simplest scenario for using Vectara is to perform a similarity search. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a8c513ab",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:25.204469Z",
+     "start_time": "2023-04-04T10:51:24.855618Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "found_docs = vectara.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fc516993",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:25.220984Z",
+     "start_time": "2023-04-04T10:51:25.213943Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And 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. A former top litigator in private practice. A former federal public defender.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(found_docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bda9bf5",
+   "metadata": {},
+   "source": [
+    "## Similarity search with score\n",
+    "\n",
+    "Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8804a21d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:25.631585Z",
+     "start_time": "2023-04-04T10:51:25.227384Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "found_docs = vectara.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "756a6887",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:25.642282Z",
+     "start_time": "2023-04-04T10:51:25.635947Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And 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. A former top litigator in private practice. A former federal public defender.\n",
+      "\n",
+      "Score: 1.0046461\n"
+     ]
+    }
+   ],
+   "source": [
+    "document, score = found_docs[0]\n",
+    "print(document.page_content)\n",
+    "print(f\"\\nScore: {score}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "691a82d6",
+   "metadata": {},
+   "source": [
+    "## Vectara as a Retriever\n",
+    "\n",
+    "Vectara, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9427195f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:26.031451Z",
+     "start_time": "2023-04-04T10:51:26.018763Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VectorStoreRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x156d3e830>, search_type='similarity', search_kwargs={})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = vectara.as_retriever()\n",
+    "retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f3c70c31",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-04-04T10:51:26.495652Z",
+     "start_time": "2023-04-04T10:51:26.046407Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='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. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And 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. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "retriever.get_relevant_documents(query)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2300e785",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/models/chat/integrations/google_vertex_ai_palm.ipynb

@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Platform Vertex AI PaLM \n",
+    "\n",
+    "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
+    "\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
+    "\n",
+    "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
+    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+    "\n",
+    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+    "\n",
+    "For more information, see: \n",
+    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install google-cloud-aiplatform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.chat_models import ChatVertexAI\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    "    SystemMessage\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatVertexAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Sure, here is the translation of the sentence \"I love programming\" from English to French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "    HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
+    "\n",
+    "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
+    "human_template=\"{text}\"\n",
+    "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Sure, here is the translation of \"I love programming\" in French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
+    "\n",
+    "# get a chat completion from the formatted messages\n",
+    "chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())"
+   ]
+  },
+  {
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/modules/models/llms/examples/human_input_llm.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How (and why) to use the the human input LLM\n",
+    "# How (and why) to use the human input LLM\n",
     "\n",
     "Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.\n",
     "\n",
@@ -34,6 +34,23 @@
     "from langchain.agents import AgentType"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we will use the `WikipediaQueryRun` tool in this notebook, you might need to install the `wikipedia` package if you haven't done so already."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install wikipedia"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -217,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.9"
   },
   "orig_nbformat": 4,
   "vscode": {

docs/modules/models/llms/integrations/beam.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "J-yvaDTmTTza"
+   },
+   "source": [
+    "# Beam integration for langchain\n",
+    "\n",
+    "Calls the Beam API wrapper to deploy and make subsequent calls to an instance of the gpt2 LLM in a cloud deployment. Requires installation of the Beam library and registration of Beam Client ID and Client Secret. By calling the wrapper an instance of the model is created and run, with returned text relating to the prompt. Additional calls can then be made by directly calling the Beam API.\n",
+    "\n",
+    "[Create an account](https://www.beam.cloud/), if you don't have one already. Grab your API keys from the [dashboard](https://www.beam.cloud/dashboard/settings/api-keys)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CfTmesWtTfTS"
+   },
+   "source": [
+    "Install the Beam CLI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "G_tCCurqR7Ik"
+   },
+   "outputs": [],
+   "source": [
+    "!curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jJkcNqOdThQ7"
+   },
+   "source": [
+    "Register API Keys and set your beam client id and secret environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7gQd6fszSEaH"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import subprocess\n",
+    "\n",
+    "beam_client_id = \"<Your beam client id>\"\n",
+    "beam_client_secret = \"<Your beam client secret>\"\n",
+    "\n",
+    "# Set the environment variables\n",
+    "os.environ['BEAM_CLIENT_ID'] = beam_client_id\n",
+    "os.environ['BEAM_CLIENT_SECRET'] = beam_client_secret\n",
+    "\n",
+    "# Run the beam configure command\n",
+    "!beam configure --clientId={beam_client_id} --clientSecret={beam_client_secret}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c20rkK18TrK2"
+   },
+   "source": [
+    "Install the Beam SDK:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CH2Vop6ISNIf"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install beam-sdk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XflOsp3bTwl1"
+   },
+   "source": [
+    "**Deploy and call Beam directly from langchain!**\n",
+    "\n",
+    "Note that a cold start might take a couple of minutes to return the response, but subsequent calls will be faster!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KmaHxUqbSVnh"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms.beam import Beam\n",
+    "\n",
+    "llm = Beam(model_name=\"gpt2\",\n",
+    "           name=\"langchain-gpt2-test\",\n",
+    "           cpu=8,\n",
+    "           memory=\"32Gi\",\n",
+    "           gpu=\"A10G\",\n",
+    "           python_version=\"python3.8\",\n",
+    "           python_packages=[\n",
+    "               \"diffusers[torch]>=0.10\",\n",
+    "               \"transformers\",\n",
+    "               \"torch\",\n",
+    "               \"pillow\",\n",
+    "               \"accelerate\",\n",
+    "               \"safetensors\",\n",
+    "               \"xformers\",],\n",
+    "           max_length=\"50\",\n",
+    "           verbose=False)\n",
+    "\n",
+    "llm._deploy()\n",
+    "\n",
+    "response = llm._call(\"Running machine learning on a remote GPU\")\n",
+    "\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "private_outputs": true,
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/modules/models/llms/integrations/google_vertex_ai_palm.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Platform Vertex AI PaLM \n",
+    "\n",
+    "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
+    "\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
+    "\n",
+    "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
+    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+    "\n",
+    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+    "\n",
+    "For more information, see: \n",
+    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install google-cloud-aiplatform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.llms import VertexAI\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = VertexAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Justin Bieber was born on March 1, 1994. The Super Bowl in 1994 was won by the San Francisco 49ers.\\nThe final answer: San Francisco 49ers.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/modules/models/llms/integrations/mosaicml.ipynb

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MosaicML\n",
+    "\n",
+    "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with MosaicML Inference for text completion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "MOSAICML_API_TOKEN = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"MOSAICML_API_TOKEN\"] = MOSAICML_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import MosaicML\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Question: {question}\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = MosaicML(inject_instruction_format=True, model_kwargs={'do_sample': False})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"What is one good reason why you should train a large language model on domain specific data?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/models/llms/integrations/openlm.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# OpenLM\n",
+    "[OpenLM](https://github.com/r2d4/openlm) is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP. \n",
+    "\n",
+    "\n",
+    "It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code.\n",
+    "\n",
+    "This examples goes over how to use LangChain to interact with both OpenAI and HuggingFace. You'll need API keys from both."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "Install dependencies and set API keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment to install openlm and openai if you haven't already\n",
+    "\n",
+    "# !pip install openlm\n",
+    "# !pip install openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "import os\n",
+    "import subprocess\n",
+    "\n",
+    "\n",
+    "# Check if OPENAI_API_KEY environment variable is set\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    print(\"Enter your OpenAI API key:\")\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "\n",
+    "# Check if HF_API_TOKEN environment variable is set\n",
+    "if \"HF_API_TOKEN\" not in os.environ:\n",
+    "    print(\"Enter your HuggingFace Hub API key:\")\n",
+    "    os.environ[\"HF_API_TOKEN\"] = getpass()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using LangChain with OpenLM\n",
+    "\n",
+    "Here we're going to call two models in an LLMChain, `text-davinci-003` from OpenAI and `gpt2` on HuggingFace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenLM\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: text-davinci-003\n",
+      "Result:  France is a country in Europe. The capital of France is Paris.\n",
+      "Model: huggingface.co/gpt2\n",
+      "Result: Question: What is the capital of France?\n",
+      "\n",
+      "Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"What is the capital of France?\"\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "\n",
+    "for model in [\"text-davinci-003\", \"huggingface.co/gpt2\"]:\n",
+    "    llm = OpenLM(model=model)\n",
+    "    llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "    result = llm_chain.run(question)\n",
+    "    print(\"\"\"Model: {}\n",
+    "Result: {}\"\"\".format(model, result))"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/models/llms/integrations/rellm_experimental.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Structured Decoding with RELLM\n",
     "\n",
-    "[RELLM](https://github.com/r2d4/rellm) is a library that wraps local HuggingFace pipeline models for structured decoding.\n",
+    "[RELLM](https://github.com/r2d4/rellm) is a library that wraps local Hugging Face pipeline models for structured decoding.\n",
     "\n",
     "It works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression.\n",
     "\n",
@@ -32,7 +32,7 @@
    "id": "66bd89f1-8daa-433d-bb8f-5b0b3ae34b00",
    "metadata": {},
    "source": [
-    "### HuggingFace Baseline\n",
+    "### Hugging Face Baseline\n",
     "\n",
     "First, let's establish a qualitative baseline by checking the output of the model without structured decoding."
    ]

docs/modules/models/text_embedding/examples/elasticsearch.ipynb

@@ -0,0 +1,124 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip -q install elasticsearch langchain"
+      ],
+      "metadata": {
+        "id": "6dJxqebov4eU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import elasticsearch\n",
+        "from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings"
+      ],
+      "metadata": {
+        "id": "RV7C3DUmv4aq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the model ID\n",
+        "model_id = 'your_model_id'"
+      ],
+      "metadata": {
+        "id": "MrT3jplJvp09"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Instantiate ElasticsearchEmbeddings using credentials\n",
+        "embeddings = ElasticsearchEmbeddings.from_credentials(\n",
+        "    model_id,\n",
+        "    es_cloud_id='your_cloud_id', \n",
+        "    es_user='your_user', \n",
+        "    es_password='your_password'\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "svtdnC-dvpxR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create embeddings for multiple documents\n",
+        "documents = [\n",
+        "    'This is an example document.', \n",
+        "    'Another example document to generate embeddings for.'\n",
+        "]\n",
+        "document_embeddings = embeddings.embed_documents(documents)\n"
+      ],
+      "metadata": {
+        "id": "7DXZAK7Kvpth"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Print document embeddings\n",
+        "for i, embedding in enumerate(document_embeddings):\n",
+        "    print(f\"Embedding for document {i+1}: {embedding}\")\n"
+      ],
+      "metadata": {
+        "id": "K8ra75W_vpqy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create an embedding for a single query\n",
+        "query = 'This is a single query.'\n",
+        "query_embedding = embeddings.embed_query(query)\n"
+      ],
+      "metadata": {
+        "id": "V4Q5kQo9vpna"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Print query embedding\n",
+        "print(f\"Embedding for query: {query_embedding}\")\n"
+      ],
+      "metadata": {
+        "id": "O0oQDzGKvpkz"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

docs/modules/models/text_embedding/examples/google_vertex_ai_palm.ipynb

@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Platform Vertex AI PaLM \n",
+    "\n",
+    "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
+    "\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
+    "\n",
+    "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
+    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+    "\n",
+    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+    "\n",
+    "For more information, see: \n",
+    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install google-cloud-aiplatform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.embeddings import VertexAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = VertexAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text])"
+   ]
+  }
+ ],
+ "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/modules/models/text_embedding/examples/minimax.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MiniMax\n",
+    "\n",
+    "[MiniMax](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) offers an embeddings service.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with MiniMax Inference for text embedding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:15.397075Z",
+     "start_time": "2023-05-24T15:13:15.387540Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
+    "os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:17.176956Z",
+     "start_time": "2023-05-24T15:13:15.399076Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import MiniMaxEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:17.193751Z",
+     "start_time": "2023-05-24T15:13:17.182053Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "embeddings = MiniMaxEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:17.844903Z",
+     "start_time": "2023-05-24T15:13:17.198751Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "query_text = \"This is a test query.\"\n",
+    "query_result = embeddings.embed_query(query_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:18.605339Z",
+     "start_time": "2023-05-24T15:13:17.845906Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "document_text = \"This is a test document.\"\n",
+    "document_result = embeddings.embed_documents([document_text])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-24T15:13:18.620432Z",
+     "start_time": "2023-05-24T15:13:18.608335Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity between document and query: 0.1573236279277012\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "query_numpy = np.array(query_result)\n",
+    "document_numpy = np.array(document_result[0])\n",
+    "similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
+    "print(f\"Cosine similarity between document and query: {similarity}\")"
+   ]
+  },
+  {
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/models/text_embedding/examples/modelscope_hub.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ModelScope\n",
+    "\n",
+    "Let's load the ModelScope Embedding class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import ModelScopeEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_id = \"damo/nlp_corom_sentence-embedding_english-base\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = ModelScopeEmbeddings(model_id=model_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_results = embeddings.embed_documents([\"foo\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chatgpt",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/models/text_embedding/examples/mosaicml.ipynb

@@ -0,0 +1,109 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MosaicML embeddings\n",
+    "\n",
+    "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with MosaicML Inference for text embedding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "MOSAICML_API_TOKEN = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"MOSAICML_API_TOKEN\"] = MOSAICML_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import MosaicMLInstructorEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = MosaicMLInstructorEmbeddings(\n",
+    "    query_instruction=\"Represent the query for retrieval: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_text = \"This is a test query.\"\n",
+    "query_result = embeddings.embed_query(query_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_text = \"This is a test document.\"\n",
+    "document_result = embeddings.embed_documents([document_text])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "query_numpy = np.array(query_result)\n",
+    "document_numpy = np.array(document_result[0])\n",
+    "similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
+    "print(f\"Cosine similarity between document and query: {similarity}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/prompts/prompt_templates/getting_started.md

@@ -150,7 +150,6 @@ In this example, we'll create a prompt to generate word antonyms.
 ```python
 from langchain import PromptTemplate, FewShotPromptTemplate
 
-
 # First, create the list of few shot examples.
 examples = [
     {"word": "happy", "antonym": "sad"},
@@ -159,10 +158,10 @@ examples = [
 
 # Next, we specify the template to format the examples we have provided.
 # We use the `PromptTemplate` class for this.
-example_formatter_template = """
-Word: {word}
-Antonym: {antonym}\n
+example_formatter_template = """Word: {word}
+Antonym: {antonym}
 """
+
 example_prompt = PromptTemplate(
     input_variables=["word", "antonym"],
     template=example_formatter_template,
@@ -176,14 +175,14 @@ few_shot_prompt = FewShotPromptTemplate(
     example_prompt=example_prompt,
     # The prefix is some text that goes before the examples in the prompt.
     # Usually, this consists of intructions.
-    prefix="Give the antonym of every input",
+    prefix="Give the antonym of every input\n",
     # The suffix is some text that goes after the examples in the prompt.
     # Usually, this is where the user input will go
-    suffix="Word: {input}\nAntonym:",
+    suffix="Word: {input}\nAntonym: ",
     # The input variables are the variables that the overall prompt expects.
     input_variables=["input"],
     # The example_separator is the string we will use to join the prefix, examples, and suffix together with.
-    example_separator="\n\n",
+    example_separator="\n",
 )
 
 # We can now generate a prompt using the `format` method.
@@ -197,7 +196,7 @@ print(few_shot_prompt.format(input="big"))
 # -> Antonym: short
 # ->
 # -> Word: big
-# -> Antonym:
+# -> Antonym: 
 ```
 
 ## Select examples for a prompt template
@@ -229,7 +228,11 @@ example_selector = LengthBasedExampleSelector(
     example_prompt=example_prompt, 
     # This is the maximum length that the formatted examples should be.
     # Length is measured by the get_text_length function below.
-    max_length=25,
+    max_length=25
+    # This is the function used to get the length of a string, which is used
+    # to determine which examples to include. It is commented out because
+    # it is provided as a default value if none is specified.
+    # get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
 )
 
 # We can now use the `example_selector` to create a `FewShotPromptTemplate`.

langchain/agents/agent_toolkits/__init__.py

@@ -1,5 +1,8 @@
 """Agent toolkits."""
 
+from langchain.agents.agent_toolkits.azure_cognitive_services.toolkit import (
+    AzureCognitiveServicesToolkit,
+)
 from langchain.agents.agent_toolkits.csv.base import create_csv_agent
 from langchain.agents.agent_toolkits.file_management.toolkit import (
     FileManagementToolkit,
@@ -60,4 +63,5 @@ __all__ = [
     "JiraToolkit",
     "FileManagementToolkit",
     "PlayWrightBrowserToolkit",
+    "AzureCognitiveServicesToolkit",
 ]

langchain/agents/agent_toolkits/azure_cognitive_services/__init__.py

@@ -0,0 +1,7 @@
+"""Azure Cognitive Services Toolkit."""
+
+from langchain.agents.agent_toolkits.azure_cognitive_services.toolkit import (
+    AzureCognitiveServicesToolkit,
+)
+
+__all__ = ["AzureCognitiveServicesToolkit"]

langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import sys
+from typing import List
+
+from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.tools.azure_cognitive_services import (
+    AzureCogsFormRecognizerTool,
+    AzureCogsImageAnalysisTool,
+    AzureCogsSpeech2TextTool,
+    AzureCogsText2SpeechTool,
+)
+from langchain.tools.base import BaseTool
+
+
+class AzureCognitiveServicesToolkit(BaseToolkit):
+    """Toolkit for Azure Cognitive Services."""
+
+    def get_tools(self) -> List[BaseTool]:
+        """Get the tools in the toolkit."""
+
+        tools = [
+            AzureCogsFormRecognizerTool(),
+            AzureCogsSpeech2TextTool(),
+            AzureCogsText2SpeechTool(),
+        ]
+
+        # TODO: Remove check once azure-ai-vision supports MacOS.
+        if sys.platform.startswith("linux") or sys.platform.startswith("win"):
+            tools.append(AzureCogsImageAnalysisTool())
+        return tools

langchain/agents/chat/output_parser.py

@@ -1,8 +1,8 @@
-import json
 from typing import Union
 
 from langchain.agents.agent import AgentOutputParser
 from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
+from langchain.output_parsers.json import parse_json_markdown
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 FINAL_ANSWER_ACTION = "Final Answer:"
@@ -18,8 +18,7 @@ class ChatOutputParser(AgentOutputParser):
                 {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
             )
         try:
-            action = text.split("```")[1]
-            response = json.loads(action.strip())
+            response = parse_json_markdown(text)
             return AgentAction(response["action"], response["action_input"], text)
 
         except Exception:

langchain/agents/conversational_chat/output_parser.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-import json
 from typing import Union
 
 from langchain.agents import AgentOutputParser
 from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
+from langchain.output_parsers.json import parse_json_markdown
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
@@ -14,19 +14,7 @@ class ConvoOutputParser(AgentOutputParser):
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         try:
-            cleaned_output = text.strip()
-            if "```json" in cleaned_output:
-                _, cleaned_output = cleaned_output.split("```json")
-            if "```" in cleaned_output:
-                cleaned_output, _ = cleaned_output.split("```")
-            if cleaned_output.startswith("```json"):
-                cleaned_output = cleaned_output[len("```json") :]
-            if cleaned_output.startswith("```"):
-                cleaned_output = cleaned_output[len("```") :]
-            if cleaned_output.endswith("```"):
-                cleaned_output = cleaned_output[: -len("```")]
-            cleaned_output = cleaned_output.strip()
-            response = json.loads(cleaned_output)
+            response = parse_json_markdown(text)
             action, action_input = response["action"], response["action_input"]
             if action == "Final Answer":
                 return AgentFinish({"output": action_input}, text)

langchain/agents/self_ask_with_search/output_parser.py

@@ -1,24 +1,21 @@
-from typing import Union
+from typing import Sequence, Union
 
 from langchain.agents.agent import AgentOutputParser
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
 class SelfAskOutputParser(AgentOutputParser):
+    followups: Sequence[str] = ("Follow up:", "Followup:")
+    finish_string: str = "So the final answer is: "
+
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        followup = "Follow up:"
         last_line = text.split("\n")[-1]
-
-        if followup not in last_line:
-            finish_string = "So the final answer is: "
-            if finish_string not in last_line:
+        if not any([follow in last_line for follow in self.followups]):
+            if self.finish_string not in last_line:
                 raise OutputParserException(f"Could not parse output: {text}")
-            return AgentFinish({"output": last_line[len(finish_string) :]}, text)
+            return AgentFinish({"output": last_line[len(self.finish_string) :]}, text)
 
-        after_colon = text.split(":")[-1]
-
-        if " " == after_colon[0]:
-            after_colon = after_colon[1:]
+        after_colon = text.split(":")[-1].strip()
         return AgentAction("Intermediate Answer", after_colon, text)
 
     @property

langchain/base_language.py

@@ -58,6 +58,16 @@ class BaseLanguageModel(BaseModel, ABC):
     ) -> BaseMessage:
         """Predict message from messages."""
 
+    @abstractmethod
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        """Predict text from text."""
+
+    @abstractmethod
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        """Predict message from messages."""
+
     def get_token_ids(self, text: str) -> List[int]:
         """Get the token present in the text."""
         return _get_token_ids_default_method(text)

langchain/callbacks/__init__.py

@@ -12,6 +12,7 @@ from langchain.callbacks.openai_info import OpenAICallbackHandler
 from langchain.callbacks.stdout import StdOutCallbackHandler
 from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
 from langchain.callbacks.wandb_callback import WandbCallbackHandler
+from langchain.callbacks.whylabs_callback import WhyLabsCallbackHandler
 
 __all__ = [
     "OpenAICallbackHandler",
@@ -21,6 +22,7 @@ __all__ = [
     "MlflowCallbackHandler",
     "ClearMLCallbackHandler",
     "CometCallbackHandler",
+    "WhyLabsCallbackHandler",
     "AsyncIteratorCallbackHandler",
     "get_openai_callback",
     "tracing_enabled",

langchain/callbacks/manager.py

@@ -888,7 +888,12 @@ def _configure(
                     handler.ensure_session()
                     callback_manager.add_handler(handler, True)
                 except Exception as e:
-                    logger.debug("Unable to load requested LangChainTracer", e)
+                    logger.warning(
+                        "Unable to load requested LangChainTracer."
+                        " To disable this warning,"
+                        " unset the  LANGCHAIN_TRACING_V2 environment variables.",
+                        e,
+                    )
         if open_ai is not None and not any(
             isinstance(handler, OpenAICallbackHandler)
             for handler in callback_manager.handlers

langchain/callbacks/openai_info.py

@@ -24,20 +24,36 @@ MODEL_COST_PER_1K_TOKENS = {
     "text-davinci-003": 0.02,
     "text-davinci-002": 0.02,
     "code-davinci-002": 0.02,
+    "ada-finetuned": 0.0016,
+    "babbage-finetuned": 0.0024,
+    "curie-finetuned": 0.012,
+    "davinci-finetuned": 0.12,
 }
 
 
+def standardize_model_name(
+    model_name: str,
+    is_completion: bool = False,
+) -> str:
+    model_name = model_name.lower()
+    if "ft-" in model_name:
+        return model_name.split(":")[0] + "-finetuned"
+    elif is_completion and model_name.startswith("gpt-4"):
+        return model_name + "-completion"
+    else:
+        return model_name
+
+
 def get_openai_token_cost_for_model(
     model_name: str, num_tokens: int, is_completion: bool = False
 ) -> float:
-    suffix = "-completion" if is_completion and model_name.startswith("gpt-4") else ""
-    model = model_name.lower() + suffix
-    if model not in MODEL_COST_PER_1K_TOKENS:
+    model_name = standardize_model_name(model_name, is_completion=is_completion)
+    if model_name not in MODEL_COST_PER_1K_TOKENS:
         raise ValueError(
             f"Unknown model: {model_name}. Please provide a valid OpenAI model name."
             "Known models are: " + ", ".join(MODEL_COST_PER_1K_TOKENS.keys())
         )
-    return MODEL_COST_PER_1K_TOKENS[model] * num_tokens / 1000
+    return MODEL_COST_PER_1K_TOKENS[model_name] * num_tokens / 1000
 
 
 class OpenAICallbackHandler(BaseCallbackHandler):
@@ -83,8 +99,8 @@ class OpenAICallbackHandler(BaseCallbackHandler):
         token_usage = response.llm_output["token_usage"]
         completion_tokens = token_usage.get("completion_tokens", 0)
         prompt_tokens = token_usage.get("prompt_tokens", 0)
-        model_name = response.llm_output.get("model_name")
-        if model_name and model_name in MODEL_COST_PER_1K_TOKENS:
+        model_name = standardize_model_name(response.llm_output.get("model_name", ""))
+        if model_name in MODEL_COST_PER_1K_TOKENS:
             completion_cost = get_openai_token_cost_for_model(
                 model_name, completion_tokens, is_completion=True
             )

langchain/callbacks/streaming_aiter.py

@@ -58,7 +58,8 @@ class AsyncIteratorCallbackHandler(AsyncCallbackHandler):
             )
 
             # Cancel the other task
-            other.pop().cancel()
+            if other:
+                other.pop().cancel()
 
             # Extract the value of the first completed task
             token_or_done = cast(Union[str, Literal[True]], done.pop().result())

langchain/callbacks/whylabs_callback.py

@@ -0,0 +1,203 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult
+from langchain.utils import get_from_env
+
+if TYPE_CHECKING:
+    from whylogs.api.logger.logger import Logger
+
+diagnostic_logger = logging.getLogger(__name__)
+
+
+def import_langkit(
+    sentiment: bool = False,
+    toxicity: bool = False,
+    themes: bool = False,
+) -> Any:
+    try:
+        import langkit  # noqa: F401
+        import langkit.regexes  # noqa: F401
+        import langkit.textstat  # noqa: F401
+
+        if sentiment:
+            import langkit.sentiment  # noqa: F401
+        if toxicity:
+            import langkit.toxicity  # noqa: F401
+        if themes:
+            import langkit.themes  # noqa: F401
+    except ImportError:
+        raise ImportError(
+            "To use the whylabs callback manager you need to have the `langkit` python "
+            "package installed. Please install it with `pip install langkit`."
+        )
+    return langkit
+
+
+class WhyLabsCallbackHandler(BaseCallbackHandler):
+    """WhyLabs CallbackHandler."""
+
+    def __init__(self, logger: Logger):
+        """Initiate the rolling logger"""
+        super().__init__()
+        self.logger = logger
+        diagnostic_logger.info(
+            "Initialized WhyLabs callback handler with configured whylogs Logger."
+        )
+
+    def _profile_generations(self, generations: List[Generation]) -> None:
+        for gen in generations:
+            self.logger.log({"response": gen.text})
+
+    def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        """Pass the input prompts to the logger"""
+        for prompt in prompts:
+            self.logger.log({"prompt": prompt})
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Pass the generated response to the logger."""
+        for generations in response.generations:
+            self._profile_generations(generations)
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        """Do nothing."""
+        pass
+
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        """Do nothing."""
+
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_tool_start(
+        self,
+        serialized: Dict[str, Any],
+        input_str: str,
+        **kwargs: Any,
+    ) -> None:
+        """Do nothing."""
+
+    def on_agent_action(
+        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
+    ) -> Any:
+        """Do nothing."""
+
+    def on_tool_end(
+        self,
+        output: str,
+        color: Optional[str] = None,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Do nothing."""
+
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_text(self, text: str, **kwargs: Any) -> None:
+        """Do nothing."""
+
+    def on_agent_finish(
+        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        """Run on agent end."""
+        pass
+
+    def flush(self) -> None:
+        self.logger._do_rollover()
+        diagnostic_logger.info("Flushing WhyLabs logger, writing profile...")
+
+    def close(self) -> None:
+        self.logger.close()
+        diagnostic_logger.info("Closing WhyLabs logger, see you next time!")
+
+    def __enter__(self) -> WhyLabsCallbackHandler:
+        return self
+
+    def __exit__(
+        self, exception_type: Any, exception_value: Any, traceback: Any
+    ) -> None:
+        self.close()
+
+    @classmethod
+    def from_params(
+        cls,
+        *,
+        api_key: Optional[str] = None,
+        org_id: Optional[str] = None,
+        dataset_id: Optional[str] = None,
+        sentiment: bool = False,
+        toxicity: bool = False,
+        themes: bool = False,
+    ) -> Logger:
+        """Instantiate whylogs Logger from params.
+
+        Args:
+            api_key (Optional[str]): WhyLabs API key. Optional because the preferred
+                way to specify the API key is with environment variable
+                WHYLABS_API_KEY.
+            org_id (Optional[str]): WhyLabs organization id to write profiles to.
+                If not set must be specified in environment variable
+                WHYLABS_DEFAULT_ORG_ID.
+            dataset_id (Optional[str]): The model or dataset this callback is gathering
+                telemetry for. If not set must be specified in environment variable
+                WHYLABS_DEFAULT_DATASET_ID.
+            sentiment (bool): If True will initialize a model to perform
+                sentiment analysis compound score. Defaults to False and will not gather
+                this metric.
+            toxicity (bool): If True will initialize a model to score
+                toxicity. Defaults to False and will not gather this metric.
+            themes (bool): If True will initialize a model to calculate
+                distance to configured themes. Defaults to None and will not gather this
+                metric.
+        """
+        # langkit library will import necessary whylogs libraries
+        import_langkit(sentiment=sentiment, toxicity=toxicity, themes=themes)
+
+        import whylogs as why
+        from whylogs.api.writer.whylabs import WhyLabsWriter
+        from whylogs.core.schema import DeclarativeSchema
+        from whylogs.experimental.core.metrics.udf_metric import generate_udf_schema
+
+        api_key = api_key or get_from_env("api_key", "WHYLABS_API_KEY")
+        org_id = org_id or get_from_env("org_id", "WHYLABS_DEFAULT_ORG_ID")
+        dataset_id = dataset_id or get_from_env(
+            "dataset_id", "WHYLABS_DEFAULT_DATASET_ID"
+        )
+        whylabs_writer = WhyLabsWriter(
+            api_key=api_key, org_id=org_id, dataset_id=dataset_id
+        )
+
+        langkit_schema = DeclarativeSchema(generate_udf_schema())
+        whylabs_logger = why.logger(
+            mode="rolling", interval=5, when="M", schema=langkit_schema
+        )
+
+        whylabs_logger.append_writer(writer=whylabs_writer)
+        diagnostic_logger.info(
+            "Started whylogs Logger with WhyLabsWriter and initialized LangKit. 📝"
+        )
+        return cls(whylabs_logger)

langchain/chains/combine_documents/map_reduce.py

@@ -195,9 +195,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
             for docs in new_result_doc_list:
                 new_doc = _collapse_docs(docs, _collapse_docs_func, **kwargs)
                 result_docs.append(new_doc)
-            num_tokens = self.combine_document_chain.prompt_length(
-                result_docs, **kwargs
-            )
+            num_tokens = length_func(result_docs, **kwargs)
         if self.return_intermediate_steps:
             _results = [r[self.llm_chain.output_key] for r in results]
             extra_return_dict = {"intermediate_steps": _results}

langchain/chains/graph_qa/cypher.py

@@ -8,7 +8,7 @@ from pydantic import Field
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
-from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, PROMPT
+from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.neo4j_graph import Neo4jGraph
 from langchain.prompts.base import BasePromptTemplate
@@ -45,7 +45,7 @@ class GraphCypherQAChain(Chain):
         cls,
         llm: BaseLanguageModel,
         *,
-        qa_prompt: BasePromptTemplate = PROMPT,
+        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
         cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
         **kwargs: Any,
     ) -> GraphCypherQAChain:

langchain/chains/graph_qa/prompts.py

@@ -48,3 +48,16 @@ The question is:
 CYPHER_GENERATION_PROMPT = PromptTemplate(
     input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
 )
+
+CYPHER_QA_TEMPLATE = """You are an assistant that helps to form nice and human understandable answers.
+The information part contains the provided information that you can use to construct an answer.
+The provided information is authorative, you must never doubt it or try to use your internal knowledge to correct it.
+Make it sound like the information are coming from an AI assistant, but don't add any information.
+Information:
+{context}
+
+Question: {question}
+Helpful Answer:"""
+CYPHER_QA_PROMPT = PromptTemplate(
+    input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE
+)

langchain/chains/query_constructor/base.py

@@ -22,7 +22,7 @@ from langchain.chains.query_constructor.prompt import (
     SCHEMA_WITH_LIMIT,
 )
 from langchain.chains.query_constructor.schema import AttributeInfo
-from langchain.output_parsers.structured import parse_json_markdown
+from langchain.output_parsers.json import parse_and_check_json_markdown
 from langchain.schema import BaseOutputParser, OutputParserException
 
 
@@ -33,7 +33,7 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
     def parse(self, text: str) -> StructuredQuery:
         try:
             expected_keys = ["query", "filter"]
-            parsed = parse_json_markdown(text, expected_keys)
+            parsed = parse_and_check_json_markdown(text, expected_keys)
             if len(parsed["query"]) == 0:
                 parsed["query"] = " "
             if parsed["filter"] == "NO_FILTER" or not parsed["filter"]:

langchain/chains/router/llm_router.py

@@ -9,7 +9,7 @@ from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains import LLMChain
 from langchain.chains.router.base import RouterChain
-from langchain.output_parsers.structured import parse_json_markdown
+from langchain.output_parsers.json import parse_and_check_json_markdown
 from langchain.prompts import BasePromptTemplate
 from langchain.schema import BaseOutputParser, OutputParserException
 
@@ -77,7 +77,7 @@ class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
     def parse(self, text: str) -> Dict[str, Any]:
         try:
             expected_keys = ["destination", "next_inputs"]
-            parsed = parse_json_markdown(text, expected_keys)
+            parsed = parse_and_check_json_markdown(text, expected_keys)
             if not isinstance(parsed["destination"], str):
                 raise ValueError("Expected 'destination' to be a string.")
             if not isinstance(parsed["next_inputs"], self.next_inputs_type):

langchain/chat_models/__init__.py

@@ -3,6 +3,7 @@ from langchain.chat_models.azure_openai import AzureChatOpenAI
 from langchain.chat_models.google_palm import ChatGooglePalm
 from langchain.chat_models.openai import ChatOpenAI
 from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
+from langchain.chat_models.vertexai import ChatVertexAI
 
 __all__ = [
     "ChatOpenAI",
@@ -10,4 +11,5 @@ __all__ = [
     "PromptLayerChatOpenAI",
     "ChatAnthropic",
     "ChatGooglePalm",
+    "ChatVertexAI",
 ]

langchain/chat_models/base.py

@@ -183,6 +183,19 @@ class BaseChatModel(BaseLanguageModel, ABC):
         else:
             raise ValueError("Unexpected generation type")
 
+    async def _call_async(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        callbacks: Callbacks = None,
+    ) -> BaseMessage:
+        result = await self.agenerate([messages], stop=stop, callbacks=callbacks)
+        generation = result.generations[0][0]
+        if isinstance(generation, ChatGeneration):
+            return generation.message
+        else:
+            raise ValueError("Unexpected generation type")
+
     def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:
         return self.predict(message, stop=stop)
 
@@ -203,6 +216,23 @@ class BaseChatModel(BaseLanguageModel, ABC):
             _stop = list(stop)
         return self(messages, stop=_stop)
 
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        result = await self._call_async([HumanMessage(content=text)], stop=_stop)
+        return result.content
+
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        return await self._call_async(messages, stop=_stop)
+
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""

langchain/chat_models/vertexai.py

@@ -0,0 +1,137 @@
+"""Wrapper around Google VertexAI chat-based models."""
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional
+
+from pydantic import root_validator
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain.chat_models.base import BaseChatModel
+from langchain.llms.vertexai import _VertexAICommon
+from langchain.schema import (
+    AIMessage,
+    BaseMessage,
+    ChatGeneration,
+    ChatResult,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain.utilities.vertexai import raise_vertex_import_error
+
+
+@dataclass
+class _MessagePair:
+    """InputOutputTextPair represents a pair of input and output texts."""
+
+    question: HumanMessage
+    answer: AIMessage
+
+
+@dataclass
+class _ChatHistory:
+    """InputOutputTextPair represents a pair of input and output texts."""
+
+    history: List[_MessagePair] = field(default_factory=list)
+    system_message: Optional[SystemMessage] = None
+
+
+def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory:
+    """Parse a sequence of messages into history.
+
+    A sequence should be either (SystemMessage, HumanMessage, AIMessage,
+    HumanMessage, AIMessage, ...) or (HumanMessage, AIMessage, HumanMessage,
+    AIMessage, ...).
+
+    Args:
+        history: The list of messages to re-create the history of the chat.
+    Returns:
+        A parsed chat history.
+    Raises:
+        ValueError: If a sequence of message is odd, or a human message is not followed
+            by a message from AI (e.g., Human, Human, AI or AI, AI, Human).
+    """
+    if not history:
+        return _ChatHistory()
+    first_message = history[0]
+    system_message = first_message if isinstance(first_message, SystemMessage) else None
+    chat_history = _ChatHistory(system_message=system_message)
+    messages_left = history[1:] if system_message else history
+    if len(messages_left) % 2 != 0:
+        raise ValueError(
+            f"Amount of messages in history should be even, got {len(messages_left)}!"
+        )
+    for question, answer in zip(messages_left[::2], messages_left[1::2]):
+        if not isinstance(question, HumanMessage) or not isinstance(answer, AIMessage):
+            raise ValueError(
+                "A human message should follow a bot one, "
+                f"got {question.type}, {answer.type}."
+            )
+        chat_history.history.append(_MessagePair(question=question, answer=answer))
+    return chat_history
+
+
+class ChatVertexAI(_VertexAICommon, BaseChatModel):
+    """Wrapper around Vertex AI large language models."""
+
+    model_name: str = "chat-bison"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+        cls._try_init_vertexai(values)
+        try:
+            from vertexai.preview.language_models import ChatModel
+        except ImportError:
+            raise_vertex_import_error()
+        values["client"] = ChatModel.from_pretrained(values["model_name"])
+        return values
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    ) -> ChatResult:
+        """Generate next turn in the conversation.
+
+        Args:
+            messages: The history of the conversation as a list of messages.
+            stop: The list of stop words (optional).
+            run_manager: The Callbackmanager for LLM run, it's not used at the moment.
+
+        Returns:
+            The ChatResult that contains outputs generated by the model.
+
+        Raises:
+            ValueError: if the last message in the list is not from human.
+        """
+        if not messages:
+            raise ValueError(
+                "You should provide at least one message to start the chat!"
+            )
+        question = messages[-1]
+        if not isinstance(question, HumanMessage):
+            raise ValueError(
+                f"Last message in the list should be from human, got {question.type}."
+            )
+
+        history = _parse_chat_history(messages[:-1])
+        context = history.system_message.content if history.system_message else None
+        chat = self.client.start_chat(context=context, **self._default_params)
+        for pair in history.history:
+            chat._history.append((pair.question.content, pair.answer.content))
+        response = chat.send_message(question.content)
+        text = self._enforce_stop_words(response.text, stop)
+        return ChatResult(generations=[ChatGeneration(message=AIMessage(content=text))])
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    ) -> ChatResult:
+        raise NotImplementedError(
+            """Vertex AI doesn't support async requests at the moment."""
+        )

langchain/cli/docker-compose.yaml

@@ -19,6 +19,7 @@ services:
       - PORT=1984
       - LANGCHAIN_ENV=local_docker
       - LOG_LEVEL=warning
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
     ports:
       - 1984:1984
     depends_on:

langchain/cli/main.py

@@ -1,4 +1,5 @@
 import argparse
+import json
 import logging
 import os
 import shutil
@@ -6,7 +7,7 @@ import subprocess
 from contextlib import contextmanager
 from pathlib import Path
 from subprocess import CalledProcessError
-from typing import Generator, List, Optional
+from typing import Dict, Generator, List, Mapping, Optional, Union, cast
 
 import requests
 import yaml
@@ -19,6 +20,50 @@ logger = logging.getLogger(__name__)
 _DIR = Path(__file__).parent
 
 
+def pprint_services(services_status: List[Mapping[str, Union[str, List[str]]]]) -> None:
+    # Loop through and collect Service, State, and Publishers["PublishedPorts"]
+    # for each service
+    services = []
+    for service in services_status:
+        service_status: Dict[str, str] = {
+            "Service": str(service["Service"]),
+            "Status": str(service["Status"]),
+        }
+        publishers = cast(List[Dict], service.get("Publishers", []))
+        if publishers:
+            service_status["PublishedPorts"] = ", ".join(
+                [str(publisher["PublishedPort"]) for publisher in publishers]
+            )
+        services.append(service_status)
+
+    max_service_len = max(len(service["Service"]) for service in services)
+    max_state_len = max(len(service["Status"]) for service in services)
+    service_message = [
+        "\n"
+        + "Service".ljust(max_service_len + 2)
+        + "Status".ljust(max_state_len + 2)
+        + "Published Ports"
+    ]
+    for service in services:
+        service_str = service["Service"].ljust(max_service_len + 2)
+        state_str = service["Status"].ljust(max_state_len + 2)
+        ports_str = service.get("PublishedPorts", "")
+        service_message.append(service_str + state_str + ports_str)
+
+    langchain_endpoint: str = "http://localhost:1984"
+    used_ngrok = any(["ngrok" in service["Service"] for service in services])
+    if used_ngrok:
+        langchain_endpoint = get_ngrok_url(auth_token=None)
+
+    service_message.append(
+        "\nTo connect, set the following environment variables"
+        " in your LangChain application:"
+        "\nLANGCHAIN_TRACING_V2=true"
+        f"\nLANGCHAIN_ENDPOINT={langchain_endpoint}"
+    )
+    logger.info("\n".join(service_message))
+
+
 def get_docker_compose_command() -> List[str]:
     """Get the correct docker compose command for this system."""
     try:
@@ -173,6 +218,7 @@ class PlusCommand:
         expose: bool = False,
         auth_token: Optional[str] = None,
         dev: bool = False,
+        openai_api_key: Optional[str] = None,
     ) -> None:
         """Run the LangChainPlus server locally.
 
@@ -180,9 +226,16 @@ class PlusCommand:
             expose: If True, expose the server to the internet using ngrok.
             auth_token: The ngrok authtoken to use (visible in the ngrok dashboard).
                 If not provided, ngrok server session length will be restricted.
+            dev: If True, use the development (rc) image of LangChainPlus.
+            openai_api_key: The OpenAI API key to use for LangChainPlus
+                If not provided, the OpenAI API Key will be read from the
+                OPENAI_API_KEY environment variable. If neither are provided,
+                some features of LangChainPlus will not be available.
         """
         if dev:
             os.environ["_LANGCHAINPLUS_IMAGE_PREFIX"] = "rc-"
+        if openai_api_key is not None:
+            os.environ["OPENAI_API_KEY"] = openai_api_key
         if expose:
             self._start_and_expose(auth_token=auth_token)
         else:
@@ -214,6 +267,36 @@ class PlusCommand:
             ]
         )
 
+    def status(self) -> None:
+        """Provide information about the status LangChainPlus server."""
+
+        command = [
+            *self.docker_compose_command,
+            "-f",
+            str(self.docker_compose_file),
+            "ps",
+            "--format",
+            "json",
+        ]
+
+        result = subprocess.run(
+            command,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+        )
+        try:
+            command_stdout = result.stdout.decode("utf-8")
+            services_status = json.loads(command_stdout)
+        except json.JSONDecodeError:
+            logger.error("Error checking LangChainPlus server status.")
+            return
+        if services_status:
+            logger.info("The LangChainPlus server is currently running.")
+            pprint_services(services_status)
+        else:
+            logger.info("The LangChainPlus server is not running.")
+            return
+
 
 def env() -> None:
     """Print the runtime environment information."""
@@ -250,9 +333,20 @@ def main() -> None:
         action="store_true",
         help="Use the development version of the LangChainPlus image.",
     )
+    server_start_parser.add_argument(
+        "--openai-api-key",
+        default=os.getenv("OPENAI_API_KEY"),
+        help="The OpenAI API key to use for LangChainPlus."
+        " If not provided, the OpenAI API Key will be read from the"
+        " OPENAI_API_KEY environment variable. If neither are provided,"
+        " some features of LangChainPlus will not be available.",
+    )
     server_start_parser.set_defaults(
         func=lambda args: server_command.start(
-            expose=args.expose, auth_token=args.ngrok_authtoken, dev=args.dev
+            expose=args.expose,
+            auth_token=args.ngrok_authtoken,
+            dev=args.dev,
+            openai_api_key=args.openai_api_key,
         )
     )
 
@@ -265,7 +359,10 @@ def main() -> None:
         "logs", description="Show the LangChainPlus server logs."
     )
     server_logs_parser.set_defaults(func=lambda args: server_command.logs())
-
+    server_status_parser = server_subparsers.add_parser(
+        "status", description="Show the LangChainPlus server status."
+    )
+    server_status_parser.set_defaults(func=lambda args: server_command.status())
     env_parser = subparsers.add_parser("env")
     env_parser.set_defaults(func=lambda args: env())
 

langchain/client/langchain.py

@@ -200,7 +200,7 @@ class LangChainPlusClient(BaseSettings):
         return Dataset(**result)
 
     @retry(stop=stop_after_attempt(3), wait=wait_fixed(0.5))
-    def read_run(self, run_id: str) -> Run:
+    def read_run(self, run_id: Union[str, UUID]) -> Run:
         """Read a run from the LangChain+ API."""
         response = self._get(f"/runs/{run_id}")
         raise_for_status_with_text(response)
@@ -268,6 +268,22 @@ class LangChainPlusClient(BaseSettings):
         raise_for_status_with_text(response)
         yield from [TracerSession(**session) for session in response.json()]
 
+    @xor_args(("session_name", "session_id"))
+    def delete_session(
+        self, *, session_name: Optional[str] = None, session_id: Optional[str] = None
+    ) -> None:
+        """Delete a session from the LangChain+ API."""
+        if session_name is not None:
+            session_id = self.read_session(session_name=session_name).id
+        elif session_id is None:
+            raise ValueError("Must provide session_name or session_id")
+        response = requests.delete(
+            self.api_url + f"/sessions/{session_id}",
+            headers=self._headers,
+        )
+        raise_for_status_with_text(response)
+        return None
+
     def create_dataset(self, dataset_name: str, description: str) -> Dataset:
         """Create a dataset in the LangChain+ API."""
         dataset = DatasetCreate(
@@ -342,7 +358,7 @@ class LangChainPlusClient(BaseSettings):
     ) -> Example:
         """Create a dataset example in the LangChain+ API."""
         if dataset_id is None:
-            dataset_id = self.read_dataset(dataset_name).id
+            dataset_id = self.read_dataset(dataset_name=dataset_name).id
 
         data = {
             "inputs": inputs,
@@ -360,7 +376,7 @@ class LangChainPlusClient(BaseSettings):
         return Example(**result)
 
     @retry(stop=stop_after_attempt(3), wait=wait_fixed(0.5))
-    def read_example(self, example_id: str) -> Example:
+    def read_example(self, example_id: Union[str, UUID]) -> Example:
         """Read an example from the LangChain+ API."""
         response = self._get(f"/examples/{example_id}")
         raise_for_status_with_text(response)

langchain/document_loaders/__init__.py

@@ -10,6 +10,7 @@ from langchain.document_loaders.azure_blob_storage_container import (
 from langchain.document_loaders.azure_blob_storage_file import (
     AzureBlobStorageFileLoader,
 )
+from langchain.document_loaders.bibtex import BibtexLoader
 from langchain.document_loaders.bigquery import BigQueryLoader
 from langchain.document_loaders.bilibili import BiliBiliLoader
 from langchain.document_loaders.blackboard import BlackboardLoader
@@ -46,6 +47,7 @@ from langchain.document_loaders.ifixit import IFixitLoader
 from langchain.document_loaders.image import UnstructuredImageLoader
 from langchain.document_loaders.image_captions import ImageCaptionLoader
 from langchain.document_loaders.imsdb import IMSDbLoader
+from langchain.document_loaders.joplin import JoplinLoader
 from langchain.document_loaders.json_loader import JSONLoader
 from langchain.document_loaders.markdown import UnstructuredMarkdownLoader
 from langchain.document_loaders.mastodon import MastodonTootsLoader
@@ -100,6 +102,7 @@ from langchain.document_loaders.unstructured import (
 from langchain.document_loaders.url import UnstructuredURLLoader
 from langchain.document_loaders.url_playwright import PlaywrightURLLoader
 from langchain.document_loaders.url_selenium import SeleniumURLLoader
+from langchain.document_loaders.weather import WeatherDataLoader
 from langchain.document_loaders.web_base import WebBaseLoader
 from langchain.document_loaders.whatsapp_chat import WhatsAppChatLoader
 from langchain.document_loaders.wikipedia import WikipediaLoader
@@ -127,6 +130,7 @@ __all__ = [
     "AzureBlobStorageContainerLoader",
     "AzureBlobStorageFileLoader",
     "BSHTMLLoader",
+    "BibtexLoader",
     "BigQueryLoader",
     "BiliBiliLoader",
     "BlackboardLoader",
@@ -159,6 +163,7 @@ __all__ = [
     "IFixitLoader",
     "IMSDbLoader",
     "ImageCaptionLoader",
+    "JoplinLoader",
     "JSONLoader",
     "MWDumpLoader",
     "MastodonTootsLoader",
@@ -212,6 +217,7 @@ __all__ = [
     "UnstructuredRTFLoader",
     "UnstructuredURLLoader",
     "UnstructuredWordDocumentLoader",
+    "WeatherDataLoader",
     "WebBaseLoader",
     "WhatsAppChatLoader",
     "WikipediaLoader",

langchain/document_loaders/bibtex.py

@@ -0,0 +1,108 @@
+import logging
+import re
+from pathlib import Path
+from typing import Any, Iterator, List, Mapping, Optional
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from langchain.utilities.bibtex import BibtexparserWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class BibtexLoader(BaseLoader):
+    """Loads a bibtex file into a list of Documents.
+
+    Each document represents one entry from the bibtex file.
+
+    If a PDF file is present in the `file` bibtex field, the original PDF
+    is loaded into the document text. If no such file entry is present,
+    the `abstract` field is used instead.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        *,
+        parser: Optional[BibtexparserWrapper] = None,
+        max_docs: Optional[int] = None,
+        max_content_chars: Optional[int] = 4_000,
+        load_extra_metadata: bool = False,
+        file_pattern: str = r"[^:]+\.pdf",
+    ):
+        """Initialize the BibtexLoader.
+
+        Args:
+            file_path: Path to the bibtex file.
+            max_docs: Max number of associated documents to load. Use -1 means
+                           no limit.
+        """
+        self.file_path = file_path
+        self.parser = parser or BibtexparserWrapper()
+        self.max_docs = max_docs
+        self.max_content_chars = max_content_chars
+        self.load_extra_metadata = load_extra_metadata
+        self.file_regex = re.compile(file_pattern)
+
+    def _load_entry(self, entry: Mapping[str, Any]) -> Optional[Document]:
+        import fitz
+
+        parent_dir = Path(self.file_path).parent
+        # regex is useful for Zotero flavor bibtex files
+        file_names = self.file_regex.findall(entry.get("file", ""))
+        if not file_names:
+            return None
+        texts: List[str] = []
+        for file_name in file_names:
+            try:
+                with fitz.open(parent_dir / file_name) as f:
+                    texts.extend(page.get_text() for page in f)
+            except FileNotFoundError as e:
+                logger.debug(e)
+        content = "\n".join(texts) or entry.get("abstract", "")
+        if self.max_content_chars:
+            content = content[: self.max_content_chars]
+        metadata = self.parser.get_metadata(entry, load_extra=self.load_extra_metadata)
+        return Document(
+            page_content=content,
+            metadata=metadata,
+        )
+
+    def lazy_load(self) -> Iterator[Document]:
+        """Load bibtex file using bibtexparser and get the article texts plus the
+
+        article metadata.
+
+        See https://bibtexparser.readthedocs.io/en/master/
+
+        Returns:
+            a list of documents with the document.page_content in text format
+        """
+        try:
+            import fitz  # noqa: F401
+        except ImportError:
+            raise ImportError(
+                "PyMuPDF package not found, please install it with "
+                "`pip install pymupdf`"
+            )
+
+        entries = self.parser.load_bibtex_entries(self.file_path)
+        if self.max_docs:
+            entries = entries[: self.max_docs]
+        for entry in entries:
+            doc = self._load_entry(entry)
+            if doc:
+                yield doc
+
+    def load(self) -> List[Document]:
+        """Load bibtex file documents from the given bibtex file path.
+
+        See https://bibtexparser.readthedocs.io/en/master/
+
+        Args:
+            file_path: the path to the bibtex file
+
+        Returns:
+            a list of documents with the document.page_content in text format
+        """
+        return list(self.lazy_load())

langchain/document_loaders/googledrive.py

@@ -31,6 +31,7 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
     file_ids: Optional[List[str]] = None
     recursive: bool = False
     file_types: Optional[Sequence[str]] = None
+    load_trashed_files: bool = False
 
     @root_validator
     def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
@@ -215,8 +216,10 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
             _files = files
 
         returns = []
-        for file in _files:
-            if file["mimeType"] == "application/vnd.google-apps.document":
+        for file in files:
+            if file["trashed"] and not self.load_trashed_files:
+                continue
+            elif file["mimeType"] == "application/vnd.google-apps.document":
                 returns.append(self._load_document_from_id(file["id"]))  # type: ignore
             elif file["mimeType"] == "application/vnd.google-apps.spreadsheet":
                 returns.extend(self._load_sheet_from_id(file["id"]))  # type: ignore
@@ -224,7 +227,6 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
                 returns.extend(self._load_file_from_id(file["id"]))  # type: ignore
             else:
                 pass
-
         return returns
 
     def _fetch_files_recursive(
@@ -238,7 +240,7 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
                 pageSize=1000,
                 includeItemsFromAllDrives=True,
                 supportsAllDrives=True,
-                fields="nextPageToken, files(id, name, mimeType, parents)",
+                fields="nextPageToken, files(id, name, mimeType, parents, trashed)",
             )
             .execute()
         )

langchain/document_loaders/iugu.py

@@ -0,0 +1,41 @@
+"""Loader that fetches data from IUGU"""
+import json
+import urllib.request
+from typing import List, Optional
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from langchain.utils import get_from_env, stringify_dict
+
+IUGU_ENDPOINTS = {
+    "invoices": "https://api.iugu.com/v1/invoices",
+    "customers": "https://api.iugu.com/v1/customers",
+    "charges": "https://api.iugu.com/v1/charges",
+    "subscriptions": "https://api.iugu.com/v1/subscriptions",
+    "plans": "https://api.iugu.com/v1/plans",
+}
+
+
+class IuguLoader(BaseLoader):
+    def __init__(self, resource: str, api_token: Optional[str] = None) -> None:
+        self.resource = resource
+        api_token = api_token or get_from_env("api_token", "IUGU_API_TOKEN")
+        self.headers = {"Authorization": f"Bearer {api_token}"}
+
+    def _make_request(self, url: str) -> List[Document]:
+        request = urllib.request.Request(url, headers=self.headers)
+
+        with urllib.request.urlopen(request) as response:
+            json_data = json.loads(response.read().decode())
+            text = stringify_dict(json_data)
+            metadata = {"source": url}
+            return [Document(page_content=text, metadata=metadata)]
+
+    def _get_resource(self) -> List[Document]:
+        endpoint = IUGU_ENDPOINTS.get(self.resource)
+        if endpoint is None:
+            return []
+        return self._make_request(endpoint)
+
+    def load(self) -> List[Document]:
+        return self._get_resource()

langchain/document_loaders/joplin.py

@@ -0,0 +1,88 @@
+import json
+import urllib
+from datetime import datetime
+from typing import Iterator, List, Optional
+
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+from langchain.utils import get_from_env
+
+LINK_NOTE_TEMPLATE = "joplin://x-callback-url/openNote?id={id}"
+
+
+class JoplinLoader(BaseLoader):
+    """
+    Loader that fetches notes from Joplin.
+
+    In order to use this loader, you need to have Joplin running with the
+    Web Clipper enabled (look for "Web Clipper" in the app settings).
+
+    To get the access token, you need to go to the Web Clipper options and
+    under "Advanced Options" you will find the access token.
+
+    You can find more information about the Web Clipper service here:
+    https://joplinapp.org/clipper/
+    """
+
+    def __init__(
+        self,
+        access_token: Optional[str] = None,
+        port: int = 41184,
+        host: str = "localhost",
+    ) -> None:
+        access_token = access_token or get_from_env(
+            "access_token", "JOPLIN_ACCESS_TOKEN"
+        )
+        base_url = f"http://{host}:{port}"
+        self._get_note_url = (
+            f"{base_url}/notes?token={access_token}"
+            "&fields=id,parent_id,title,body,created_time,updated_time&page={{page}}"
+        )
+        self._get_folder_url = (
+            f"{base_url}/folders/{{id}}?token={access_token}&fields=title"
+        )
+        self._get_tag_url = (
+            f"{base_url}/notes/{{id}}/tags?token={access_token}&fields=title"
+        )
+
+    def _get_notes(self) -> Iterator[Document]:
+        has_more = True
+        page = 1
+        while has_more:
+            req_note = urllib.request.Request(self._get_note_url.format(page=page))
+            with urllib.request.urlopen(req_note) as response:
+                json_data = json.loads(response.read().decode())
+                for note in json_data["items"]:
+                    metadata = {
+                        "source": LINK_NOTE_TEMPLATE.format(id=note["id"]),
+                        "folder": self._get_folder(note["parent_id"]),
+                        "tags": self._get_tags(note["id"]),
+                        "title": note["title"],
+                        "created_time": self._convert_date(note["created_time"]),
+                        "updated_time": self._convert_date(note["updated_time"]),
+                    }
+                    yield Document(page_content=note["body"], metadata=metadata)
+
+                has_more = json_data["has_more"]
+                page += 1
+
+    def _get_folder(self, folder_id: str) -> str:
+        req_folder = urllib.request.Request(self._get_folder_url.format(id=folder_id))
+        with urllib.request.urlopen(req_folder) as response:
+            json_data = json.loads(response.read().decode())
+            return json_data["title"]
+
+    def _get_tags(self, note_id: str) -> List[str]:
+        req_tag = urllib.request.Request(self._get_tag_url.format(id=note_id))
+        with urllib.request.urlopen(req_tag) as response:
+            json_data = json.loads(response.read().decode())
+            return [tag["title"] for tag in json_data["items"]]
+
+    def _convert_date(self, date: int) -> str:
+        return datetime.fromtimestamp(date / 1000).strftime("%Y-%m-%d %H:%M:%S")
+
+    def lazy_load(self) -> Iterator[Document]:
+        yield from self._get_notes()
+
+    def load(self) -> List[Document]:
+        return list(self.lazy_load())

langchain/document_loaders/readthedocs.py

@@ -1,6 +1,6 @@
 """Loader that loads ReadTheDocs documentation directory dump."""
 from pathlib import Path
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Tuple, Union
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
@@ -11,12 +11,31 @@ class ReadTheDocsLoader(BaseLoader):
 
     def __init__(
         self,
-        path: str,
+        path: Union[str, Path],
         encoding: Optional[str] = None,
         errors: Optional[str] = None,
+        custom_html_tag: Optional[Tuple[str, dict]] = None,
         **kwargs: Optional[Any]
     ):
-        """Initialize path."""
+        """
+        Initialize ReadTheDocsLoader
+
+        The loader loops over all files under `path` and extract the actual content of
+        the files by retrieving main html tags. Default main html tags include
+        `<main id="main-content>`, <`div role="main>`, and `<article role="main">`. You
+        can also define your own html tags by passing custom_html_tag, e.g.
+        `("div", "class=main")`. The loader iterates html tags with the order of
+        custom html tags (if exists) and default html tags. If any of the tags is not
+        empty, the loop will break and retrieve the content out of that tag.
+
+        Args:
+            path: The location of pulled readthedocs folder.
+            encoding: The encoding with which to open the documents.
+            errors: Specifies how encoding and decoding errors are to be handled—this
+                cannot be used in binary mode.
+            custom_html_tag: Optional custom html tag to retrieve the content from
+                files.
+        """
         try:
             from bs4 import BeautifulSoup
         except ImportError:
@@ -32,34 +51,50 @@ class ReadTheDocsLoader(BaseLoader):
         except Exception as e:
             raise ValueError("Parsing kwargs do not appear valid") from e
 
-        self.file_path = path
+        self.file_path = Path(path)
         self.encoding = encoding
         self.errors = errors
+        self.custom_html_tag = custom_html_tag
         self.bs_kwargs = kwargs
 
     def load(self) -> List[Document]:
         """Load documents."""
-        from bs4 import BeautifulSoup
-
-        def _clean_data(data: str) -> str:
-            soup = BeautifulSoup(data, **self.bs_kwargs)
-            text = soup.find_all("main", {"id": "main-content"})
-
-            if len(text) == 0:
-                text = soup.find_all("div", {"role": "main"})
-
-            if len(text) != 0:
-                text = text[0].get_text()
-            else:
-                text = ""
-            return "\n".join([t for t in text.split("\n") if t])
-
         docs = []
-        for p in Path(self.file_path).rglob("*"):
+        for p in self.file_path.rglob("*"):
             if p.is_dir():
                 continue
             with open(p, encoding=self.encoding, errors=self.errors) as f:
-                text = _clean_data(f.read())
+                text = self._clean_data(f.read())
             metadata = {"source": str(p)}
             docs.append(Document(page_content=text, metadata=metadata))
         return docs
+
+    def _clean_data(self, data: str) -> str:
+        from bs4 import BeautifulSoup
+
+        soup = BeautifulSoup(data, **self.bs_kwargs)
+
+        # default tags
+        html_tags = [
+            ("div", {"role": "main"}),
+            ("main", {"id": "main-content"}),
+        ]
+
+        if self.custom_html_tag is not None:
+            html_tags.append(self.custom_html_tag)
+
+        text = None
+
+        # reversed order. check the custom one first
+        for tag, attrs in html_tags[::-1]:
+            text = soup.find(tag, attrs)
+            # if found, break
+            if text is not None:
+                break
+
+        if text is not None:
+            text = text.get_text()
+        else:
+            text = ""
+        # trim empty lines
+        return "\n".join([t for t in text.split("\n") if t])

langchain/document_loaders/weather.py

@@ -0,0 +1,50 @@
+"""Simple reader that reads weather data from OpenWeatherMap API"""
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Iterator, List, Optional, Sequence
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
+
+
+class WeatherDataLoader(BaseLoader):
+    """Weather Reader.
+
+    Reads the forecast & current weather of any location using OpenWeatherMap's free
+    API. Checkout 'https://openweathermap.org/appid' for more on how to generate a free
+    OpenWeatherMap API.
+    """
+
+    def __init__(
+        self,
+        client: OpenWeatherMapAPIWrapper,
+        places: Sequence[str],
+    ) -> None:
+        """Initialize with parameters."""
+        super().__init__()
+        self.client = client
+        self.places = places
+
+    @classmethod
+    def from_params(
+        cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None
+    ) -> WeatherDataLoader:
+        client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key)
+        return cls(client, places)
+
+    def lazy_load(
+        self,
+    ) -> Iterator[Document]:
+        """Lazily load weather data for the given locations."""
+        for place in self.places:
+            metadata = {"queried_at": datetime.now()}
+            content = self.client.run(place)
+            yield Document(page_content=content, metadata=metadata)
+
+    def load(
+        self,
+    ) -> List[Document]:
+        """Load weather data for the given locations."""
+        return list(self.lazy_load())

langchain/embeddings/__init__.py

@@ -7,6 +7,7 @@ from langchain.embeddings.aleph_alpha import (
     AlephAlphaSymmetricSemanticEmbedding,
 )
 from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings
 from langchain.embeddings.fake import FakeEmbeddings
 from langchain.embeddings.google_palm import GooglePalmEmbeddings
 from langchain.embeddings.huggingface import (
@@ -16,6 +17,9 @@ from langchain.embeddings.huggingface import (
 from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
 from langchain.embeddings.jina import JinaEmbeddings
 from langchain.embeddings.llamacpp import LlamaCppEmbeddings
+from langchain.embeddings.minimax import MiniMaxEmbeddings
+from langchain.embeddings.modelscope_hub import ModelScopeEmbeddings
+from langchain.embeddings.mosaicml import MosaicMLInstructorEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
 from langchain.embeddings.self_hosted import SelfHostedEmbeddings
@@ -25,6 +29,7 @@ from langchain.embeddings.self_hosted_hugging_face import (
 )
 from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
+from langchain.embeddings.vertexai import VertexAIEmbeddings
 
 logger = logging.getLogger(__name__)
 
@@ -32,12 +37,15 @@ __all__ = [
     "OpenAIEmbeddings",
     "HuggingFaceEmbeddings",
     "CohereEmbeddings",
+    "ElasticsearchEmbeddings",
     "JinaEmbeddings",
     "LlamaCppEmbeddings",
     "HuggingFaceHubEmbeddings",
+    "ModelScopeEmbeddings",
     "TensorflowHubEmbeddings",
     "SagemakerEndpointEmbeddings",
     "HuggingFaceInstructEmbeddings",
+    "MosaicMLInstructorEmbeddings",
     "SelfHostedEmbeddings",
     "SelfHostedHuggingFaceEmbeddings",
     "SelfHostedHuggingFaceInstructEmbeddings",
@@ -46,6 +54,8 @@ __all__ = [
     "AlephAlphaSymmetricSemanticEmbedding",
     "SentenceTransformerEmbeddings",
     "GooglePalmEmbeddings",
+    "MiniMaxEmbeddings",
+    "VertexAIEmbeddings",
 ]
 
 

langchain/embeddings/elasticsearch.py

@@ -0,0 +1,155 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, List, Optional
+
+from langchain.utils import get_from_env
+
+if TYPE_CHECKING:
+    from elasticsearch.client import MlClient
+
+from langchain.embeddings.base import Embeddings
+
+
+class ElasticsearchEmbeddings(Embeddings):
+    """
+    Wrapper around Elasticsearch embedding models.
+
+    This class provides an interface to generate embeddings using a model deployed
+    in an Elasticsearch cluster. It requires an Elasticsearch connection object
+    and the model_id of the model deployed in the cluster.
+
+    In Elasticsearch you need to have an embedding model loaded and deployed.
+    - https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
+    - https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
+    """  # noqa: E501
+
+    def __init__(
+        self,
+        client: MlClient,
+        model_id: str,
+        *,
+        input_field: str = "text_field",
+    ):
+        """
+        Initialize the ElasticsearchEmbeddings instance.
+
+        Args:
+            client (MlClient): An Elasticsearch ML client object.
+            model_id (str): The model_id of the model deployed in the Elasticsearch
+                cluster.
+            input_field (str): The name of the key for the input text field in the
+                document. Defaults to 'text_field'.
+        """
+        self.client = client
+        self.model_id = model_id
+        self.input_field = input_field
+
+    @classmethod
+    def from_credentials(
+        cls,
+        model_id: str,
+        *,
+        es_cloud_id: Optional[str] = None,
+        es_user: Optional[str] = None,
+        es_password: Optional[str] = None,
+        input_field: str = "text_field",
+    ) -> ElasticsearchEmbeddings:
+        """Instantiate embeddings from Elasticsearch credentials.
+
+        Args:
+            model_id (str): The model_id of the model deployed in the Elasticsearch
+                cluster.
+            input_field (str): The name of the key for the input text field in the
+                document. Defaults to 'text_field'.
+            es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to.
+            es_user: (str, optional): Elasticsearch username.
+            es_password: (str, optional): Elasticsearch password.
+
+        Example Usage:
+            from langchain.embeddings import ElasticsearchEmbeddings
+
+            # Define the model ID and input field name (if different from default)
+            model_id = "your_model_id"
+            # Optional, only if different from 'text_field'
+            input_field = "your_input_field"
+
+            # Credentials can be passed in two ways. Either set the env vars
+            # ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically pulled
+            # in, or pass them in directly as kwargs.
+            embeddings = ElasticsearchEmbeddings.from_credentials(
+                model_id,
+                input_field=input_field,
+                # es_cloud_id="foo",
+                # es_user="bar",
+                # es_password="baz",
+            )
+
+            documents = [
+                "This is an example document.",
+                "Another example document to generate embeddings for.",
+            ]
+            embeddings_generator.embed_documents(documents)
+        """
+        try:
+            from elasticsearch import Elasticsearch
+            from elasticsearch.client import MlClient
+        except ImportError:
+            raise ImportError(
+                "elasticsearch package not found, please install with 'pip install "
+                "elasticsearch'"
+            )
+
+        es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
+        es_user = es_user or get_from_env("es_user", "ES_USER")
+        es_password = es_password or get_from_env("es_password", "ES_PASSWORD")
+
+        # Connect to Elasticsearch
+        es_connection = Elasticsearch(
+            cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
+        )
+        client = MlClient(es_connection)
+        return cls(client, model_id, input_field=input_field)
+
+    def _embedding_func(self, texts: List[str]) -> List[List[float]]:
+        """
+        Generate embeddings for the given texts using the Elasticsearch model.
+
+        Args:
+            texts (List[str]): A list of text strings to generate embeddings for.
+
+        Returns:
+            List[List[float]]: A list of embeddings, one for each text in the input
+                list.
+        """
+        response = self.client.infer_trained_model(
+            model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
+        )
+
+        embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
+        return embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        Generate embeddings for a list of documents.
+
+        Args:
+            texts (List[str]): A list of document text strings to generate embeddings
+                for.
+
+        Returns:
+            List[List[float]]: A list of embeddings, one for each document in the input
+                list.
+        """
+        return self._embedding_func(texts)
+
+    def embed_query(self, text: str) -> List[float]:
+        """
+        Generate an embedding for a single query text.
+
+        Args:
+            text (str): The query text to generate an embedding for.
+
+        Returns:
+            List[float]: The embedding for the input query text.
+        """
+        return self._embedding_func([text])[0]

langchain/embeddings/minimax.py

@@ -0,0 +1,163 @@
+"""Wrapper around MiniMax APIs."""
+from __future__ import annotations
+
+import logging
+from typing import Any, Callable, Dict, List, Optional
+
+import requests
+from pydantic import BaseModel, Extra, root_validator
+from tenacity import (
+    before_sleep_log,
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+)
+
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+def _create_retry_decorator() -> Callable[[Any], Any]:
+    """Returns a tenacity retry decorator."""
+
+    multiplier = 1
+    min_seconds = 1
+    max_seconds = 4
+    max_retries = 6
+
+    return retry(
+        reraise=True,
+        stop=stop_after_attempt(max_retries),
+        wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
+        before_sleep=before_sleep_log(logger, logging.WARNING),
+    )
+
+
+def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = _create_retry_decorator()
+
+    @retry_decorator
+    def _embed_with_retry(*args: Any, **kwargs: Any) -> Any:
+        return embeddings.embed(*args, **kwargs)
+
+    return _embed_with_retry(*args, **kwargs)
+
+
+class MiniMaxEmbeddings(BaseModel, Embeddings):
+    """Wrapper around MiniMax's embedding inference service.
+
+    To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
+    ``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
+    the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import MiniMaxEmbeddings
+            embeddings = MiniMaxEmbeddings()
+
+            query_text = "This is a test query."
+            query_result = embeddings.embed_query(query_text)
+
+            document_text = "This is a test document."
+            document_result = embeddings.embed_documents([document_text])
+
+    """
+
+    endpoint_url: str = "https://api.minimax.chat/v1/embeddings"
+    """Endpoint URL to use."""
+    model: str = "embo-01"
+    """Embeddings model name to use."""
+    embed_type_db: str = "db"
+    """For embed_documents"""
+    embed_type_query: str = "query"
+    """For embed_query"""
+
+    minimax_group_id: Optional[str] = None
+    """Group ID for MiniMax API."""
+    minimax_api_key: Optional[str] = None
+    """API Key for MiniMax API."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that group id and api key exists in environment."""
+        minimax_group_id = get_from_dict_or_env(
+            values, "minimax_group_id", "MINIMAX_GROUP_ID"
+        )
+        minimax_api_key = get_from_dict_or_env(
+            values, "minimax_api_key", "MINIMAX_API_KEY"
+        )
+        values["minimax_group_id"] = minimax_group_id
+        values["minimax_api_key"] = minimax_api_key
+        return values
+
+    def embed(
+        self,
+        texts: List[str],
+        embed_type: str,
+    ) -> List[List[float]]:
+        payload = {
+            "model": self.model,
+            "type": embed_type,
+            "texts": texts,
+        }
+
+        # HTTP headers for authorization
+        headers = {
+            "Authorization": f"Bearer {self.minimax_api_key}",
+            "Content-Type": "application/json",
+        }
+
+        params = {
+            "GroupId": self.minimax_group_id,
+        }
+
+        # send request
+        response = requests.post(
+            self.endpoint_url, params=params, headers=headers, json=payload
+        )
+        parsed_response = response.json()
+
+        # check for errors
+        if parsed_response["base_resp"]["status_code"] != 0:
+            raise ValueError(
+                f"MiniMax API returned an error: {parsed_response['base_resp']}"
+            )
+
+        embeddings = parsed_response["vectors"]
+
+        return embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed documents using a MiniMax embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db)
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using a MiniMax embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        embeddings = embed_with_retry(
+            self, texts=[text], embed_type=self.embed_type_query
+        )
+        return embeddings[0]

langchain/embeddings/modelscope_hub.py

@@ -0,0 +1,72 @@
+"""Wrapper around ModelScopeHub embedding models."""
+from typing import Any, List
+
+from pydantic import BaseModel, Extra
+
+from langchain.embeddings.base import Embeddings
+
+
+class ModelScopeEmbeddings(BaseModel, Embeddings):
+    """Wrapper around modelscope_hub embedding models.
+
+    To use, you should have the ``modelscope`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import ModelScopeEmbeddings
+            model_id = "damo/nlp_corom_sentence-embedding_english-base"
+            embed = ModelScopeEmbeddings(model_id=model_id)
+    """
+
+    embed: Any
+    model_id: str = "damo/nlp_corom_sentence-embedding_english-base"
+    """Model name to use."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the modelscope"""
+        super().__init__(**kwargs)
+        try:
+            from modelscope.pipelines import pipeline
+            from modelscope.utils.constant import Tasks
+
+            self.embed = pipeline(Tasks.sentence_embedding, model=self.model_id)
+
+        except ImportError as e:
+            raise ImportError(
+                "Could not import some python packages."
+                "Please install it with `pip install modelscope`."
+            ) from e
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a modelscope embedding model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        texts = list(map(lambda x: x.replace("\n", " "), texts))
+        inputs = {"source_sentence": texts}
+        embeddings = self.embed(input=inputs)["text_embedding"]
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a modelscope embedding model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        text = text.replace("\n", " ")
+        inputs = {"source_sentence": [text]}
+        embedding = self.embed(input=inputs)["text_embedding"][0]
+        return embedding.tolist()

langchain/embeddings/mosaicml.py

@@ -0,0 +1,137 @@
+"""Wrapper around MosaicML APIs."""
+from typing import Any, Dict, List, Mapping, Optional, Tuple
+
+import requests
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_dict_or_env
+
+
+class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
+    """Wrapper around MosaicML's embedding inference service.
+
+    To use, you should have the
+    environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import MosaicMLInstructorEmbeddings
+            endpoint_url = (
+                "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
+            )
+            mosaic_llm = MosaicMLInstructorEmbeddings(
+                endpoint_url=endpoint_url,
+                mosaicml_api_token="my-api-key"
+            )
+    """
+
+    endpoint_url: str = (
+        "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
+    )
+    """Endpoint URL to use."""
+    embed_instruction: str = "Represent the document for retrieval: "
+    """Instruction used to embed documents."""
+    query_instruction: str = (
+        "Represent the question for retrieving supporting documents: "
+    )
+    """Instruction used to embed the query."""
+    retry_sleep: float = 1.0
+    """How long to try sleeping for if a rate limit is encountered"""
+
+    mosaicml_api_token: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        mosaicml_api_token = get_from_dict_or_env(
+            values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
+        )
+        values["mosaicml_api_token"] = mosaicml_api_token
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {"endpoint_url": self.endpoint_url}
+
+    def _embed(
+        self, input: List[Tuple[str, str]], is_retry: bool = False
+    ) -> List[List[float]]:
+        payload = {"input_strings": input}
+
+        # HTTP headers for authorization
+        headers = {
+            "Authorization": f"{self.mosaicml_api_token}",
+            "Content-Type": "application/json",
+        }
+
+        # send request
+        try:
+            response = requests.post(self.endpoint_url, headers=headers, json=payload)
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+
+        try:
+            parsed_response = response.json()
+
+            if "error" in parsed_response:
+                # if we get rate limited, try sleeping for 1 second
+                if (
+                    not is_retry
+                    and "rate limit exceeded" in parsed_response["error"].lower()
+                ):
+                    import time
+
+                    time.sleep(self.retry_sleep)
+
+                    return self._embed(input, is_retry=True)
+
+                raise ValueError(
+                    f"Error raised by inference API: {parsed_response['error']}"
+                )
+
+            if "data" not in parsed_response:
+                raise ValueError(
+                    f"Error raised by inference API, no key data: {parsed_response}"
+                )
+            embeddings = parsed_response["data"]
+        except requests.exceptions.JSONDecodeError as e:
+            raise ValueError(
+                f"Error raised by inference API: {e}.\nResponse: {response.text}"
+            )
+
+        return embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed documents using a MosaicML deployed instructor embedding model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        instruction_pairs = [(self.embed_instruction, text) for text in texts]
+        embeddings = self._embed(instruction_pairs)
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using a MosaicML deployed instructor embedding model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        instruction_pair = (self.query_instruction, text)
+        embedding = self._embed([instruction_pair])[0]
+        return embedding

langchain/embeddings/vertexai.py

@@ -0,0 +1,47 @@
+"""Wrapper around Google VertexAI embedding models."""
+from typing import Dict, List
+
+from pydantic import root_validator
+
+from langchain.embeddings.base import Embeddings
+from langchain.llms.vertexai import _VertexAICommon
+from langchain.utilities.vertexai import raise_vertex_import_error
+
+
+class VertexAIEmbeddings(_VertexAICommon, Embeddings):
+    model_name: str = "textembedding-gecko"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validates that the python package exists in environment."""
+        cls._try_init_vertexai(values)
+        try:
+            from vertexai.preview.language_models import TextEmbeddingModel
+        except ImportError:
+            raise_vertex_import_error()
+        values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"])
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of strings.
+
+        Args:
+            texts: List[str] The list of strings to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = self.client.get_embeddings(texts)
+        return [el.values for el in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embedding for the text.
+        """
+        embeddings = self.client.get_embeddings([text])
+        return embeddings[0].values

langchain/experimental/plan_and_execute/planners/chat_planner.py

@@ -25,7 +25,7 @@ SYSTEM_PROMPT = (
 
 class PlanningOutputParser(PlanOutputParser):
     def parse(self, text: str) -> Plan:
-        steps = [Step(value=v) for v in re.split("\n\d+\. ", text)[1:]]
+        steps = [Step(value=v) for v in re.split("\n\s*\d+\. ", text)[1:]]
         return Plan(steps=steps)
 
 

langchain/indexes/graph.py

@@ -28,3 +28,15 @@ class GraphIndexCreator(BaseModel):
         for triple in knowledge:
             graph.add_triple(triple)
         return graph
+
+    async def afrom_text(self, text: str) -> NetworkxEntityGraph:
+        """Create graph index from text asynchronously."""
+        if self.llm is None:
+            raise ValueError("llm should not be None")
+        graph = self.graph_type()
+        chain = LLMChain(llm=self.llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)
+        output = await chain.apredict(text=text)
+        knowledge = parse_triples(output)
+        for triple in knowledge:
+            graph.add_triple(triple)
+        return graph

langchain/llms/__init__.py

@@ -7,6 +7,7 @@ from langchain.llms.anthropic import Anthropic
 from langchain.llms.anyscale import Anyscale
 from langchain.llms.bananadev import Banana
 from langchain.llms.base import BaseLLM
+from langchain.llms.beam import Beam
 from langchain.llms.cerebriumai import CerebriumAI
 from langchain.llms.cohere import Cohere
 from langchain.llms.deepinfra import DeepInfra
@@ -22,8 +23,10 @@ from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInfe
 from langchain.llms.human import HumanInputLLM
 from langchain.llms.llamacpp import LlamaCpp
 from langchain.llms.modal import Modal
+from langchain.llms.mosaicml import MosaicML
 from langchain.llms.nlpcloud import NLPCloud
 from langchain.llms.openai import AzureOpenAI, OpenAI, OpenAIChat
+from langchain.llms.openlm import OpenLM
 from langchain.llms.petals import Petals
 from langchain.llms.pipelineai import PipelineAI
 from langchain.llms.predictionguard import PredictionGuard
@@ -34,6 +37,7 @@ from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
 from langchain.llms.self_hosted import SelfHostedPipeline
 from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
 from langchain.llms.stochasticai import StochasticAI
+from langchain.llms.vertexai import VertexAI
 from langchain.llms.writer import Writer
 
 __all__ = [
@@ -41,6 +45,7 @@ __all__ = [
     "AlephAlpha",
     "Anyscale",
     "Banana",
+    "Beam",
     "CerebriumAI",
     "Cohere",
     "DeepInfra",
@@ -50,9 +55,11 @@ __all__ = [
     "GPT4All",
     "LlamaCpp",
     "Modal",
+    "MosaicML",
     "NLPCloud",
     "OpenAI",
     "OpenAIChat",
+    "OpenLM",
     "Petals",
     "PipelineAI",
     "HuggingFaceEndpoint",
@@ -73,6 +80,7 @@ __all__ = [
     "HumanInputLLM",
     "HuggingFaceTextGenInference",
     "FakeListLLM",
+    "VertexAI",
 ]
 
 type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
@@ -81,6 +89,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "anthropic": Anthropic,
     "anyscale": Anyscale,
     "bananadev": Banana,
+    "beam": Beam,
     "cerebriumai": CerebriumAI,
     "cohere": Cohere,
     "deepinfra": DeepInfra,
@@ -92,10 +101,12 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "huggingface_endpoint": HuggingFaceEndpoint,
     "llamacpp": LlamaCpp,
     "modal": Modal,
+    "mosaic": MosaicML,
     "sagemaker_endpoint": SagemakerEndpoint,
     "nlpcloud": NLPCloud,
     "human-input": HumanInputLLM,
     "openai": OpenAI,
+    "openlm": OpenLM,
     "petals": Petals,
     "pipelineai": PipelineAI,
     "huggingface_pipeline": HuggingFacePipeline,
@@ -108,4 +119,5 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "rwkv": RWKV,
     "huggingface_textgen_inference": HuggingFaceTextGenInference,
     "fake-list": FakeListLLM,
+    "vertexai": VertexAI,
 }

langchain/llms/base.py

@@ -299,6 +299,13 @@ class BaseLLM(BaseLanguageModel, ABC):
             .text
         )
 
+    async def _call_async(
+        self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None
+    ) -> str:
+        """Check Cache and run the LLM on the given prompt and input."""
+        result = await self.agenerate([prompt], stop=stop, callbacks=callbacks)
+        return result.generations[0][0].text
+
     def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
         if stop is None:
             _stop = None
@@ -317,6 +324,24 @@ class BaseLLM(BaseLanguageModel, ABC):
         content = self(text, stop=_stop)
         return AIMessage(content=content)
 
+    async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        return await self._call_async(text, stop=_stop)
+
+    async def apredict_messages(
+        self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
+    ) -> BaseMessage:
+        text = get_buffer_string(messages)
+        if stop is None:
+            _stop = None
+        else:
+            _stop = list(stop)
+        content = await self._call_async(text, stop=_stop)
+        return AIMessage(content=content)
+
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""

langchain/llms/beam.py

@@ -0,0 +1,268 @@
+"""Wrapper around Beam API."""
+import base64
+import json
+import logging
+import subprocess
+import textwrap
+import time
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from pydantic import Extra, Field, root_validator
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_NUM_TRIES = 10
+DEFAULT_SLEEP_TIME = 4
+
+
+class Beam(LLM):
+    """Wrapper around Beam API for gpt2 large language model.
+
+    To use, you should have the ``beam-sdk`` python package installed,
+    and the environment variable ``BEAM_CLIENT_ID`` set with your client id
+    and ``BEAM_CLIENT_SECRET`` set with your client secret. Information on how
+    to get these is available here: https://docs.beam.cloud/account/api-keys.
+
+    The wrapper can then be called as follows, where the name, cpu, memory, gpu,
+    python version, and python packages can be updated accordingly. Once deployed,
+    the instance can be called.
+        llm = Beam(model_name="gpt2",
+            name="langchain-gpt2",
+            cpu=8,
+            memory="32Gi",
+            gpu="A10G",
+            python_version="python3.8",
+            python_packages=[
+                "diffusers[torch]>=0.10",
+                "transformers",
+                "torch",
+                "pillow",
+                "accelerate",
+                "safetensors",
+                "xformers",],
+            max_length=50)
+
+        llm._deploy()
+        call_result = llm._call(input)
+    """
+
+    model_name: str = ""
+    name: str = ""
+    cpu: str = ""
+    memory: str = ""
+    gpu: str = ""
+    python_version: str = ""
+    python_packages: List[str] = []
+    max_length: str = ""
+    url: str = ""
+    """model endpoint to use"""
+
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for `create` call not
+    explicitly specified."""
+
+    beam_client_id: str = ""
+    beam_client_secret: str = ""
+    app_id: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic config."""
+
+        extra = Extra.forbid
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name not in all_required_field_names:
+                if field_name in extra:
+                    raise ValueError(f"Found {field_name} supplied twice.")
+                logger.warning(
+                    f"""{field_name} was transfered to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+        values["model_kwargs"] = extra
+        return values
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        beam_client_id = get_from_dict_or_env(
+            values, "beam_client_id", "BEAM_CLIENT_ID"
+        )
+        beam_client_secret = get_from_dict_or_env(
+            values, "beam_client_secret", "BEAM_CLIENT_SECRET"
+        )
+        values["beam_client_id"] = beam_client_id
+        values["beam_client_secret"] = beam_client_secret
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model_name": self.model_name,
+            "name": self.name,
+            "cpu": self.cpu,
+            "memory": self.memory,
+            "gpu": self.gpu,
+            "python_version": self.python_version,
+            "python_packages": self.python_packages,
+            "max_length": self.max_length,
+            "model_kwargs": self.model_kwargs,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "beam"
+
+    def app_creation(self) -> None:
+        """Creates a Python file which will contain your Beam app definition."""
+        script = textwrap.dedent(
+            """\
+        import beam
+
+        # The environment your code will run on
+        app = beam.App(
+            name="{name}",
+            cpu={cpu},
+            memory="{memory}",
+            gpu="{gpu}",
+            python_version="{python_version}",
+            python_packages={python_packages},
+        )
+
+        app.Trigger.RestAPI(
+            inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
+            outputs={{"text": beam.Types.String()}},
+            handler="run.py:beam_langchain",
+        )
+
+        """
+        )
+
+        script_name = "app.py"
+        with open(script_name, "w") as file:
+            file.write(
+                script.format(
+                    name=self.name,
+                    cpu=self.cpu,
+                    memory=self.memory,
+                    gpu=self.gpu,
+                    python_version=self.python_version,
+                    python_packages=self.python_packages,
+                )
+            )
+
+    def run_creation(self) -> None:
+        """Creates a Python file which will be deployed on beam."""
+        script = textwrap.dedent(
+            """
+        import os
+        import transformers
+        from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+        model_name = "{model_name}"
+
+        def beam_langchain(**inputs):
+            prompt = inputs["prompt"]
+            length = inputs["max_length"]
+
+            tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+            model = GPT2LMHeadModel.from_pretrained(model_name)
+            encodedPrompt = tokenizer.encode(prompt, return_tensors='pt')
+            outputs = model.generate(encodedPrompt, max_length=int(length),
+              do_sample=True, pad_token_id=tokenizer.eos_token_id)
+            output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            print(output)
+            return {{"text": output}}
+
+        """
+        )
+
+        script_name = "run.py"
+        with open(script_name, "w") as file:
+            file.write(script.format(model_name=self.model_name))
+
+    def _deploy(self) -> str:
+        """Call to Beam."""
+        try:
+            import beam  # type: ignore
+
+            if beam.__path__ == "":
+                raise ImportError
+        except ImportError:
+            raise ImportError(
+                "Could not import beam python package. "
+                "Please install it with `curl "
+                "https://raw.githubusercontent.com/slai-labs"
+                "/get-beam/main/get-beam.sh -sSfL | sh`."
+            )
+        self.app_creation()
+        self.run_creation()
+
+        process = subprocess.run(
+            "beam deploy app.py", shell=True, capture_output=True, text=True
+        )
+
+        if process.returncode == 0:
+            output = process.stdout
+            logger.info(output)
+            lines = output.split("\n")
+
+            for line in lines:
+                if line.startswith(" i  Send requests to: https://apps.beam.cloud/"):
+                    self.app_id = line.split("/")[-1]
+                    self.url = line.split(":")[1].strip()
+                    return self.app_id
+
+            raise ValueError(
+                f"""Failed to retrieve the appID from the deployment output.
+                Deployment output: {output}"""
+            )
+        else:
+            raise ValueError(f"Deployment failed. Error: {process.stderr}")
+
+    @property
+    def authorization(self) -> str:
+        if self.beam_client_id:
+            credential_str = self.beam_client_id + ":" + self.beam_client_secret
+        else:
+            credential_str = self.beam_client_secret
+        return base64.b64encode(credential_str.encode()).decode()
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[list] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    ) -> str:
+        """Call to Beam."""
+        url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
+        payload = {"prompt": prompt, "max_length": self.max_length}
+        headers = {
+            "Accept": "*/*",
+            "Accept-Encoding": "gzip, deflate",
+            "Authorization": "Basic " + self.authorization,
+            "Connection": "keep-alive",
+            "Content-Type": "application/json",
+        }
+
+        for _ in range(DEFAULT_NUM_TRIES):
+            request = requests.post(url, headers=headers, data=json.dumps(payload))
+            if request.status_code == 200:
+                return request.json()["text"]
+            time.sleep(DEFAULT_SLEEP_TIME)
+        logger.warning("Unable to successfully call model.")
+        return ""

langchain/llms/mosaicml.py

@@ -0,0 +1,173 @@
+"""Wrapper around MosaicML APIs."""
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from pydantic import Extra, root_validator
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+from langchain.utils import get_from_dict_or_env
+
+INSTRUCTION_KEY = "### Instruction:"
+RESPONSE_KEY = "### Response:"
+INTRO_BLURB = (
+    "Below is an instruction that describes a task. "
+    "Write a response that appropriately completes the request."
+)
+PROMPT_FOR_GENERATION_FORMAT = """{intro}
+{instruction_key}
+{instruction}
+{response_key}
+""".format(
+    intro=INTRO_BLURB,
+    instruction_key=INSTRUCTION_KEY,
+    instruction="{instruction}",
+    response_key=RESPONSE_KEY,
+)
+
+
+class MosaicML(LLM):
+    """Wrapper around MosaicML's LLM inference service.
+
+    To use, you should have the
+    environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import MosaicML
+            endpoint_url = (
+                "https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
+            )
+            mosaic_llm = MosaicML(
+                endpoint_url=endpoint_url,
+                mosaicml_api_token="my-api-key"
+            )
+    """
+
+    endpoint_url: str = (
+        "https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
+    )
+    """Endpoint URL to use."""
+    inject_instruction_format: bool = False
+    """Whether to inject the instruction format into the prompt."""
+    model_kwargs: Optional[dict] = None
+    """Key word arguments to pass to the model."""
+    retry_sleep: float = 1.0
+    """How long to try sleeping for if a rate limit is encountered"""
+
+    mosaicml_api_token: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        mosaicml_api_token = get_from_dict_or_env(
+            values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
+        )
+        values["mosaicml_api_token"] = mosaicml_api_token
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        _model_kwargs = self.model_kwargs or {}
+        return {
+            **{"endpoint_url": self.endpoint_url},
+            **{"model_kwargs": _model_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "mosaicml"
+
+    def _transform_prompt(self, prompt: str) -> str:
+        """Transform prompt."""
+        if self.inject_instruction_format:
+            prompt = PROMPT_FOR_GENERATION_FORMAT.format(
+                instruction=prompt,
+            )
+        return prompt
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        is_retry: bool = False,
+    ) -> str:
+        """Call out to a MosaicML LLM inference endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = mosaic_llm("Tell me a joke.")
+        """
+        _model_kwargs = self.model_kwargs or {}
+
+        prompt = self._transform_prompt(prompt)
+
+        payload = {"input_strings": [prompt]}
+        payload.update(_model_kwargs)
+
+        # HTTP headers for authorization
+        headers = {
+            "Authorization": f"{self.mosaicml_api_token}",
+            "Content-Type": "application/json",
+        }
+
+        # send request
+        try:
+            response = requests.post(self.endpoint_url, headers=headers, json=payload)
+        except requests.exceptions.RequestException as e:
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+
+        try:
+            parsed_response = response.json()
+
+            if "error" in parsed_response:
+                # if we get rate limited, try sleeping for 1 second
+                if (
+                    not is_retry
+                    and "rate limit exceeded" in parsed_response["error"].lower()
+                ):
+                    import time
+
+                    time.sleep(self.retry_sleep)
+
+                    return self._call(prompt, stop, run_manager, is_retry=True)
+
+                raise ValueError(
+                    f"Error raised by inference API: {parsed_response['error']}"
+                )
+
+            if "data" not in parsed_response:
+                raise ValueError(
+                    f"Error raised by inference API, no key data: {parsed_response}"
+                )
+            generated_text = parsed_response["data"]
+        except requests.exceptions.JSONDecodeError as e:
+            raise ValueError(
+                f"Error raised by inference API: {e}.\nResponse: {response.text}"
+            )
+
+        text = generated_text[0][len(prompt) :]
+
+        # TODO: replace when MosaicML supports custom stop tokens natively
+        if stop is not None:
+            text = enforce_stop_tokens(text, stop)
+        return text

langchain/llms/openai.py

@@ -512,6 +512,10 @@ class BaseOpenAI(BaseLLM):
             "code-cushman-001": 2048,
         }
 
+        # handling finetuned models
+        if "ft-" in modelname:
+            modelname = modelname.split(":")[0]
+
         context_size = model_token_mapping.get(modelname, None)
 
         if context_size is None:

langchain/llms/openlm.py

@@ -0,0 +1,26 @@
+from typing import Any, Dict
+
+from pydantic import root_validator
+
+from langchain.llms.openai import BaseOpenAI
+
+
+class OpenLM(BaseOpenAI):
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        return {**{"model": self.model_name}, **super()._invocation_params}
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        try:
+            import openlm
+
+            values["client"] = openlm.Completion
+        except ImportError:
+            raise ValueError(
+                "Could not import openlm python package. "
+                "Please install it with `pip install openlm`."
+            )
+        if values["streaming"]:
+            raise ValueError("Streaming not supported with openlm")
+        return values

langchain/llms/vertexai.py

@@ -0,0 +1,110 @@
+"""Wrapper around Google VertexAI models."""
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from pydantic import BaseModel, root_validator
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+from langchain.utilities.vertexai import (
+    init_vertexai,
+    raise_vertex_import_error,
+)
+
+if TYPE_CHECKING:
+    from google.auth.credentials import Credentials
+    from vertexai.language_models._language_models import _LanguageModel
+
+
+class _VertexAICommon(BaseModel):
+    client: "_LanguageModel" = None  #: :meta private:
+    model_name: str
+    "Model name to use."
+    temperature: float = 0.0
+    "Sampling temperature, it controls the degree of randomness in token selection."
+    max_output_tokens: int = 128
+    "Token limit determines the maximum amount of text output from one prompt."
+    top_p: float = 0.95
+    "Tokens are selected from most probable to least until the sum of their "
+    "probabilities equals the top-p value."
+    top_k: int = 40
+    "How the model selects tokens for output, the next token is selected from "
+    "among the top-k most probable tokens."
+    project: Optional[str] = None
+    "The default GCP project to use when making Vertex API calls."
+    location: str = "us-central1"
+    "The default location to use when making API calls."
+    credentials: Optional["Credentials"] = None
+    "The default custom credentials to use when making API calls. If not provided "
+    "credentials will be ascertained from the environment." ""
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        base_params = {
+            "temperature": self.temperature,
+            "max_output_tokens": self.max_output_tokens,
+            "top_k": self.top_p,
+            "top_p": self.top_k,
+        }
+        return {**base_params}
+
+    def _predict(self, prompt: str, stop: Optional[List[str]]) -> str:
+        res = self.client.predict(prompt, **self._default_params)
+        return self._enforce_stop_words(res.text, stop)
+
+    def _enforce_stop_words(self, text: str, stop: Optional[List[str]]) -> str:
+        if stop:
+            return enforce_stop_tokens(text, stop)
+        return text
+
+    @property
+    def _llm_type(self) -> str:
+        return "vertexai"
+
+    @classmethod
+    def _try_init_vertexai(cls, values: Dict) -> None:
+        allowed_params = ["project", "location", "credentials"]
+        params = {k: v for k, v in values.items() if v in allowed_params}
+        init_vertexai(**params)
+        return None
+
+
+class VertexAI(_VertexAICommon, LLM):
+    """Wrapper around Google Vertex AI large language models."""
+
+    model_name: str = "text-bison"
+    tuned_model_name: Optional[str] = None
+    "The name of a tuned model, if it's provided, model_name is ignored."
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+        cls._try_init_vertexai(values)
+        try:
+            from vertexai.preview.language_models import TextGenerationModel
+        except ImportError:
+            raise_vertex_import_error()
+        tuned_model_name = values.get("tuned_model_name")
+        if tuned_model_name:
+            values["client"] = TextGenerationModel.get_tuned_model(tuned_model_name)
+        else:
+            values["client"] = TextGenerationModel.from_pretrained(values["model_name"])
+        return values
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    ) -> str:
+        """Call Vertex model to get predictions based on the prompt.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: A list of stop words (optional).
+            run_manager: A Callbackmanager for LLM run, optional.
+
+        Returns:
+            The string generated by the model.
+        """
+        return self._predict(prompt, stop)

langchain/memory/chat_message_histories/cassandra.py

@@ -7,6 +7,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
 )
 
@@ -159,7 +160,7 @@ class CassandraChatMessageHistory(BaseChatMessageHistory):
             self.session.execute(
                 """INSERT INTO message_store
                 (id, session_id, history) VALUES (%s, %s, %s);""",
-                (uuid.uuid4(), self.session_id, json.dumps(message.dict())),
+                (uuid.uuid4(), self.session_id, json.dumps(_message_to_dict(message))),
             )
         except (Unavailable, WriteTimeout, WriteFailure) as error:
             logger.error("Unable to write chat history messages to cassandra")

langchain/memory/chat_message_histories/dynamodb.py

@@ -6,6 +6,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
     messages_to_dict,
 )
@@ -63,7 +64,7 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory):
         from botocore.exceptions import ClientError
 
         messages = messages_to_dict(self.messages)
-        _message = message.dict()
+        _message = _message_to_dict(message)
         messages.append(_message)
 
         try:

langchain/memory/chat_message_histories/mongodb.py

@@ -7,6 +7,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
 )
 
@@ -81,7 +82,7 @@ class MongoDBChatMessageHistory(BaseChatMessageHistory):
             self.collection.insert_one(
                 {
                     "SessionId": self.session_id,
-                    "History": json.dumps(message.dict()),
+                    "History": json.dumps(_message_to_dict(message)),
                 }
             )
         except errors.WriteError as err:

langchain/memory/chat_message_histories/postgres.py

@@ -7,6 +7,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
 )
 
@@ -67,7 +68,9 @@ class PostgresChatMessageHistory(BaseChatMessageHistory):
         query = sql.SQL("INSERT INTO {} (session_id, message) VALUES (%s, %s);").format(
             sql.Identifier(self.table_name)
         )
-        self.cursor.execute(query, (self.session_id, json.dumps(message.dict())))
+        self.cursor.execute(
+            query, (self.session_id, json.dumps(_message_to_dict(message)))
+        )
         self.connection.commit()
 
     def clear(self) -> None:

langchain/memory/chat_message_histories/redis.py

@@ -7,6 +7,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
 )
 
@@ -59,7 +60,7 @@ class RedisChatMessageHistory(BaseChatMessageHistory):
 
     def append(self, message: BaseMessage) -> None:
         """Append the message to the record in Redis"""
-        self.redis_client.lpush(self.key, json.dumps(message.dict()))
+        self.redis_client.lpush(self.key, json.dumps(_message_to_dict(message)))
         if self.ttl:
             self.redis_client.expire(self.key, self.ttl)
 

langchain/memory/chat_message_histories/sql.py

@@ -11,6 +11,7 @@ from langchain.schema import (
     BaseChatMessageHistory,
     BaseMessage,
     HumanMessage,
+    _message_to_dict,
     messages_from_dict,
 )
 
@@ -69,7 +70,7 @@ class SQLChatMessageHistory(BaseChatMessageHistory):
     def append(self, message: BaseMessage) -> None:
         """Append the message to the record in db"""
         with self.Session() as session:
-            jsonstr = json.dumps(message.dict())
+            jsonstr = json.dumps(_message_to_dict(message))
             session.add(self.Message(session_id=self.session_id, message=jsonstr))
             session.commit()
 

langchain/output_parsers/json.py

@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+import json
+from typing import List
+
+from langchain.schema import OutputParserException
+
+
+def parse_json_markdown(json_string: str) -> dict:
+    # Remove the triple backticks if present
+    json_string = json_string.replace("```json", "").replace("```", "")
+
+    # Strip whitespace and newlines from the start and end
+    json_string = json_string.strip()
+
+    # Parse the JSON string into a Python dictionary
+    parsed = json.loads(json_string)
+
+    return parsed
+
+
+def parse_and_check_json_markdown(text: str, expected_keys: List[str]) -> dict:
+    try:
+        json_obj = parse_json_markdown(text)
+    except json.JSONDecodeError as e:
+        raise OutputParserException(f"Got invalid JSON object. Error: {e}")
+    for key in expected_keys:
+        if key not in json_obj:
+            raise OutputParserException(
+                f"Got invalid return object. Expected key `{key}` "
+                f"to be present, but got {json_obj}"
+            )
+    return json_obj

langchain/output_parsers/retry.py

@@ -85,7 +85,7 @@ class RetryWithErrorOutputParser(BaseOutputParser[T]):
     """Wraps a parser and tries to fix parsing errors.
 
     Does this by passing the original prompt, the completion, AND the error
-    that was raised to another language and telling it that the completion
+    that was raised to another language model and telling it that the completion
     did not work, and raised the given error. Differs from RetryOutputParser
     in that this implementation provides the error that was raised back to the
     LLM, which in theory should give it more information on how to fix it.

langchain/output_parsers/structured.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
-import json
 from typing import Any, List
 
 from pydantic import BaseModel
 
 from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
-from langchain.schema import BaseOutputParser, OutputParserException
+from langchain.output_parsers.json import parse_and_check_json_markdown
+from langchain.schema import BaseOutputParser
 
 line_template = '\t"{name}": {type}  // {description}'
 
@@ -22,27 +22,6 @@ def _get_sub_string(schema: ResponseSchema) -> str:
     )
 
 
-def parse_json_markdown(text: str, expected_keys: List[str]) -> Any:
-    if "```json" not in text:
-        raise OutputParserException(
-            f"Got invalid return object. Expected markdown code snippet with JSON "
-            f"object, but got:\n{text}"
-        )
-
-    json_string = text.split("```json")[1].strip().strip("```").strip()
-    try:
-        json_obj = json.loads(json_string)
-    except json.JSONDecodeError as e:
-        raise OutputParserException(f"Got invalid JSON object. Error: {e}")
-    for key in expected_keys:
-        if key not in json_obj:
-            raise OutputParserException(
-                f"Got invalid return object. Expected key `{key}` "
-                f"to be present, but got {json_obj}"
-            )
-    return json_obj
-
-
 class StructuredOutputParser(BaseOutputParser):
     response_schemas: List[ResponseSchema]
 
@@ -60,7 +39,7 @@ class StructuredOutputParser(BaseOutputParser):
 
     def parse(self, text: str) -> Any:
         expected_keys = [rs.name for rs in self.response_schemas]
-        return parse_json_markdown(text, expected_keys)
+        return parse_and_check_json_markdown(text, expected_keys)
 
     @property
     def _type(self) -> str:

langchain/prompts/chat.py

@@ -1,12 +1,10 @@
 """Chat prompt template."""
 from __future__ import annotations
 
-import json
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Sequence, Tuple, Type, TypeVar, Union
+from typing import Any, Callable, List, Sequence, Tuple, Type, TypeVar, Union
 
-import yaml
 from pydantic import BaseModel, Field
 
 from langchain.memory.buffer import get_buffer_string
@@ -97,16 +95,6 @@ class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
     def input_variables(self) -> List[str]:
         return self.prompt.input_variables
 
-    @property
-    @abstractmethod
-    def _type(self) -> str:
-        """The type of MessagePromptTemplate."""
-
-    def dict(self, *args: Any, **kwargs: Any) -> dict:
-        result = super().dict(*args, **kwargs)
-        result["_type"] = self._type
-        return result
-
 
 class ChatMessagePromptTemplate(BaseStringMessagePromptTemplate):
     role: str
@@ -117,48 +105,24 @@ class ChatMessagePromptTemplate(BaseStringMessagePromptTemplate):
             content=text, role=self.role, additional_kwargs=self.additional_kwargs
         )
 
-    @property
-    def _type(self) -> str:
-        """The type of MessagePromptTemplate."""
-        return "chat-message-prompt-template"
-
 
 class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
     def format(self, **kwargs: Any) -> BaseMessage:
         text = self.prompt.format(**kwargs)
         return HumanMessage(content=text, additional_kwargs=self.additional_kwargs)
 
-    @property
-    def _type(self) -> str:
-        """The type of MessagePromptTemplate."""
-        return "human-message-prompt-template"
-
 
 class AIMessagePromptTemplate(BaseStringMessagePromptTemplate):
-    role: str = "ai"
-
     def format(self, **kwargs: Any) -> BaseMessage:
         text = self.prompt.format(**kwargs)
         return AIMessage(content=text, additional_kwargs=self.additional_kwargs)
 
-    @property
-    def _type(self) -> str:
-        """The type of MessagePromptTemplate."""
-        return "ai-message-prompt-template"
-
 
 class SystemMessagePromptTemplate(BaseStringMessagePromptTemplate):
-    role: str = "system"
-
     def format(self, **kwargs: Any) -> BaseMessage:
         text = self.prompt.format(**kwargs)
         return SystemMessage(content=text, additional_kwargs=self.additional_kwargs)
 
-    @property
-    def _type(self) -> str:
-        """The type of MessagePromptTemplate."""
-        return "system-message-prompt-template"
-
 
 class ChatPromptValue(PromptValue):
     messages: List[BaseMessage]
@@ -253,26 +217,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
 
     @property
     def _prompt_type(self) -> str:
-        """Return the prompt type key."""
-        return "chat_prompt"
+        raise NotImplementedError
 
     def save(self, file_path: Union[Path, str]) -> None:
-        if isinstance(file_path, str):
-            save_path = Path(file_path)
-        else:
-            save_path = file_path
-
-        directory_path = save_path.parent
-        directory_path.mkdir(parents=True, exist_ok=True)
-
-        # Fetch dictionary to save
-        prompt_dict = self.dict()
-
-        if save_path.suffix == ".json":
-            with open(file_path, "w") as f:
-                json.dump(prompt_dict, f, indent=4)
-        elif save_path.suffix == ".yaml":
-            with open(file_path, "w") as f:
-                yaml.dump(prompt_dict, f, default_flow_style=False)
-        else:
-            raise ValueError(f"{save_path} must be json or yaml")
+        raise NotImplementedError

langchain/prompts/loading.py

@@ -9,16 +9,8 @@ import yaml
 
 from langchain.output_parsers.regex import RegexParser
 from langchain.prompts.base import BasePromptTemplate
-from langchain.prompts.chat import (
-    AIMessagePromptTemplate,
-    BaseMessagePromptTemplate,
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-)
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
-from langchain.schema import BaseMessage, message_from_dict
 from langchain.utilities.loading import try_load_from_hub
 
 URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/prompts/"
@@ -122,35 +114,6 @@ def _load_prompt(config: dict) -> PromptTemplate:
     return PromptTemplate(**config)
 
 
-def _load_chat_prompt(config: dict) -> ChatPromptTemplate:
-    """Load the prompt template from config."""
-    # Load the template from disk if necessary.
-    config = _load_template("template", config)
-    config = _load_output_parser(config)
-
-    messages = []
-    for message in config["messages"]:
-        _type = message.pop("_type")
-        if _type == "human-message-prompt-template":
-            prompt = load_prompt_from_config(message.pop("prompt"))
-            _message: Union[
-                BaseMessagePromptTemplate, BaseMessage
-            ] = HumanMessagePromptTemplate(**{"prompt": prompt, **message})
-        elif _type == "ai-message-prompt-template":
-            prompt = load_prompt_from_config(message.pop("prompt"))
-            _message = AIMessagePromptTemplate(**{"prompt": prompt, **message})
-        elif _type == "system-message-prompt-template":
-            prompt = load_prompt_from_config(message.pop("prompt"))
-            _message = SystemMessagePromptTemplate(**{"prompt": prompt, **message})
-        elif _type == "base-message":
-            _message = message_from_dict(message)
-        else:  # role == system
-            raise ValueError
-        messages.append(_message)
-
-    return ChatPromptTemplate.from_messages(messages)
-
-
 def load_prompt(path: Union[str, Path]) -> BasePromptTemplate:
     """Unified method for loading a prompt from LangChainHub or local fs."""
     if hub_result := try_load_from_hub(
@@ -195,7 +158,6 @@ def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate:
 
 type_to_loader_dict = {
     "prompt": _load_prompt,
-    "chat_prompt": _load_chat_prompt,
     "few_shot": _load_few_shot_prompt,
     # "few_shot_with_templates": _load_few_shot_with_templates_prompt,
 }

langchain/retrievers/tfidf.py

@@ -2,7 +2,9 @@
 
 Largely based on
 https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
-from typing import Any, Dict, List, Optional
+from __future__ import annotations
+
+from typing import Any, Dict, Iterable, List, Optional
 
 from pydantic import BaseModel
 
@@ -23,18 +25,39 @@ class TFIDFRetriever(BaseRetriever, BaseModel):
     @classmethod
     def from_texts(
         cls,
-        texts: List[str],
+        texts: Iterable[str],
+        metadatas: Optional[Iterable[dict]] = None,
         tfidf_params: Optional[Dict[str, Any]] = None,
-        **kwargs: Any
-    ) -> "TFIDFRetriever":
-        from sklearn.feature_extraction.text import TfidfVectorizer
+        **kwargs: Any,
+    ) -> TFIDFRetriever:
+        try:
+            from sklearn.feature_extraction.text import TfidfVectorizer
+        except ImportError:
+            raise ImportError(
+                "Could not import scikit-learn, please install with `pip install "
+                "scikit-learn`."
+            )
 
         tfidf_params = tfidf_params or {}
         vectorizer = TfidfVectorizer(**tfidf_params)
         tfidf_array = vectorizer.fit_transform(texts)
-        docs = [Document(page_content=t) for t in texts]
+        metadatas = metadatas or ({} for _ in texts)
+        docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
         return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
 
+    @classmethod
+    def from_documents(
+        cls,
+        documents: Iterable[Document],
+        *,
+        tfidf_params: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> TFIDFRetriever:
+        texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
+        return cls.from_texts(
+            texts=texts, tfidf_params=tfidf_params, metadatas=metadatas, **kwargs
+        )
+
     def get_relevant_documents(self, query: str) -> List[Document]:
         from sklearn.metrics.pairwise import cosine_similarity
 

langchain/retrievers/time_weighted_retriever.py

@@ -127,7 +127,9 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         self, documents: List[Document], **kwargs: Any
     ) -> List[str]:
         """Add documents to vectorstore."""
-        current_time = kwargs.get("current_time", datetime.datetime.now())
+        current_time = kwargs.get("current_time")
+        if current_time is None:
+            current_time = datetime.datetime.now()
         # Avoid mutating input documents
         dup_docs = [deepcopy(d) for d in documents]
         for i, doc in enumerate(dup_docs):

langchain/schema.py

@@ -75,13 +75,6 @@ class BaseMessage(BaseModel):
     def type(self) -> str:
         """Type of the message, used for serialization."""
 
-    def dict(self, *args: Any, **kwargs: Any) -> Dict[str, Any]:
-        return {
-            "type": self.type,
-            "data": super().dict(*args, **kwargs),
-            "_type": "base-message",
-        }
-
 
 class HumanMessage(BaseMessage):
     """Type of message that is spoken by the human."""
@@ -125,11 +118,15 @@ class ChatMessage(BaseMessage):
         return "chat"
 
 
+def _message_to_dict(message: BaseMessage) -> dict:
+    return {"type": message.type, "data": message.dict()}
+
+
 def messages_to_dict(messages: List[BaseMessage]) -> List[dict]:
-    return [m.dict() for m in messages]
+    return [_message_to_dict(m) for m in messages]
 
 
-def message_from_dict(message: dict) -> BaseMessage:
+def _message_from_dict(message: dict) -> BaseMessage:
     _type = message["type"]
     if _type == "human":
         return HumanMessage(**message["data"])
@@ -144,7 +141,7 @@ def message_from_dict(message: dict) -> BaseMessage:
 
 
 def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:
-    return [message_from_dict(m) for m in messages]
+    return [_message_from_dict(m) for m in messages]
 
 
 class ChatGeneration(Generation):

langchain/text_splitter.py

@@ -64,10 +64,12 @@ class TextSplitter(BaseDocumentTransformer, ABC):
                 documents.append(new_doc)
         return documents
 
-    def split_documents(self, documents: List[Document]) -> List[Document]:
+    def split_documents(self, documents: Iterable[Document]) -> List[Document]:
         """Split documents."""
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
+        texts, metadatas = [], []
+        for doc in documents:
+            texts.append(doc.page_content)
+            metadatas.append(doc.metadata)
         return self.create_documents(texts, metadatas=metadatas)
 
     def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:

langchain/tools/__init__.py

@@ -1,5 +1,11 @@
 """Core toolkit implementations."""
 
+from langchain.tools.azure_cognitive_services import (
+    AzureCogsFormRecognizerTool,
+    AzureCogsImageAnalysisTool,
+    AzureCogsSpeech2TextTool,
+    AzureCogsText2SpeechTool,
+)
 from langchain.tools.base import BaseTool, StructuredTool, Tool, tool
 from langchain.tools.bing_search.tool import BingSearchResults, BingSearchRun
 from langchain.tools.ddg_search.tool import DuckDuckGoSearchResults, DuckDuckGoSearchRun
@@ -56,6 +62,10 @@ from langchain.tools.zapier.tool import ZapierNLAListActions, ZapierNLARunAction
 __all__ = [
     "AIPluginTool",
     "APIOperation",
+    "AzureCogsFormRecognizerTool",
+    "AzureCogsImageAnalysisTool",
+    "AzureCogsSpeech2TextTool",
+    "AzureCogsText2SpeechTool",
     "BaseTool",
     "BaseTool",
     "BaseTool",

langchain/tools/azure_cognitive_services/__init__.py

@@ -0,0 +1,21 @@
+"""Azure Cognitive Services Tools."""
+
+from langchain.tools.azure_cognitive_services.form_recognizer import (
+    AzureCogsFormRecognizerTool,
+)
+from langchain.tools.azure_cognitive_services.image_analysis import (
+    AzureCogsImageAnalysisTool,
+)
+from langchain.tools.azure_cognitive_services.speech2text import (
+    AzureCogsSpeech2TextTool,
+)
+from langchain.tools.azure_cognitive_services.text2speech import (
+    AzureCogsText2SpeechTool,
+)
+
+__all__ = [
+    "AzureCogsImageAnalysisTool",
+    "AzureCogsFormRecognizerTool",
+    "AzureCogsSpeech2TextTool",
+    "AzureCogsText2SpeechTool",
+]

langchain/tools/azure_cognitive_services/form_recognizer.py

@@ -0,0 +1,152 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+from pydantic import root_validator
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
+from langchain.tools.base import BaseTool
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class AzureCogsFormRecognizerTool(BaseTool):
+    """Tool that queries the Azure Cognitive Services Form Recognizer API.
+
+    In order to set this up, follow instructions at:
+    https://learn.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/get-started-sdks-rest-api?view=form-recog-3.0.0&pivots=programming-language-python
+    """
+
+    azure_cogs_key: str = ""  #: :meta private:
+    azure_cogs_endpoint: str = ""  #: :meta private:
+    doc_analysis_client: Any  #: :meta private:
+
+    name = "Azure Cognitive Services Form Recognizer"
+    description = (
+        "A wrapper around Azure Cognitive Services Form Recognizer. "
+        "Useful for when you need to "
+        "extract text, tables, and key-value pairs from documents. "
+        "Input should be a url to a document."
+    )
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and endpoint exists in environment."""
+        azure_cogs_key = get_from_dict_or_env(
+            values, "azure_cogs_key", "AZURE_COGS_KEY"
+        )
+
+        azure_cogs_endpoint = get_from_dict_or_env(
+            values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
+        )
+
+        try:
+            from azure.ai.formrecognizer import DocumentAnalysisClient
+            from azure.core.credentials import AzureKeyCredential
+
+            values["doc_analysis_client"] = DocumentAnalysisClient(
+                endpoint=azure_cogs_endpoint,
+                credential=AzureKeyCredential(azure_cogs_key),
+            )
+
+        except ImportError:
+            raise ImportError(
+                "azure-ai-formrecognizer is not installed. "
+                "Run `pip install azure-ai-formrecognizer` to install."
+            )
+
+        return values
+
+    def _parse_tables(self, tables: List[Any]) -> List[Any]:
+        result = []
+        for table in tables:
+            rc, cc = table.row_count, table.column_count
+            _table = [["" for _ in range(cc)] for _ in range(rc)]
+            for cell in table.cells:
+                _table[cell.row_index][cell.column_index] = cell.content
+            result.append(_table)
+        return result
+
+    def _parse_kv_pairs(self, kv_pairs: List[Any]) -> List[Any]:
+        result = []
+        for kv_pair in kv_pairs:
+            key = kv_pair.key.content if kv_pair.key else ""
+            value = kv_pair.value.content if kv_pair.value else ""
+            result.append((key, value))
+        return result
+
+    def _document_analysis(self, document_path: str) -> Dict:
+        document_src_type = detect_file_src_type(document_path)
+        if document_src_type == "local":
+            with open(document_path, "rb") as document:
+                poller = self.doc_analysis_client.begin_analyze_document(
+                    "prebuilt-document", document
+                )
+        elif document_src_type == "remote":
+            poller = self.doc_analysis_client.begin_analyze_document_from_url(
+                "prebuilt-document", document_path
+            )
+        else:
+            raise ValueError(f"Invalid document path: {document_path}")
+
+        result = poller.result()
+        res_dict = {}
+
+        if result.content is not None:
+            res_dict["content"] = result.content
+
+        if result.tables is not None:
+            res_dict["tables"] = self._parse_tables(result.tables)
+
+        if result.key_value_pairs is not None:
+            res_dict["key_value_pairs"] = self._parse_kv_pairs(result.key_value_pairs)
+
+        return res_dict
+
+    def _format_document_analysis_result(self, document_analysis_result: Dict) -> str:
+        formatted_result = []
+        if "content" in document_analysis_result:
+            formatted_result.append(
+                f"Content: {document_analysis_result['content']}".replace("\n", " ")
+            )
+
+        if "tables" in document_analysis_result:
+            for i, table in enumerate(document_analysis_result["tables"]):
+                formatted_result.append(f"Table {i}: {table}".replace("\n", " "))
+
+        if "key_value_pairs" in document_analysis_result:
+            for kv_pair in document_analysis_result["key_value_pairs"]:
+                formatted_result.append(
+                    f"{kv_pair[0]}: {kv_pair[1]}".replace("\n", " ")
+                )
+
+        return "\n".join(formatted_result)
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        try:
+            document_analysis_result = self._document_analysis(query)
+            if not document_analysis_result:
+                return "No good document analysis result was found"
+
+            return self._format_document_analysis_result(document_analysis_result)
+        except Exception as e:
+            raise RuntimeError(f"Error while running AzureCogsFormRecognizerTool: {e}")
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError("AzureCogsFormRecognizerTool does not support async")

langchain/tools/azure_cognitive_services/image_analysis.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Optional
+
+from pydantic import root_validator
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
+from langchain.tools.base import BaseTool
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class AzureCogsImageAnalysisTool(BaseTool):
+    """Tool that queries the Azure Cognitive Services Image Analysis API.
+
+    In order to set this up, follow instructions at:
+    https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40
+    """
+
+    azure_cogs_key: str = ""  #: :meta private:
+    azure_cogs_endpoint: str = ""  #: :meta private:
+    vision_service: Any  #: :meta private:
+    analysis_options: Any  #: :meta private:
+
+    name = "Azure Cognitive Services Image Analysis"
+    description = (
+        "A wrapper around Azure Cognitive Services Image Analysis. "
+        "Useful for when you need to analyze images. "
+        "Input should be a url to an image."
+    )
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and endpoint exists in environment."""
+        azure_cogs_key = get_from_dict_or_env(
+            values, "azure_cogs_key", "AZURE_COGS_KEY"
+        )
+
+        azure_cogs_endpoint = get_from_dict_or_env(
+            values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
+        )
+
+        try:
+            import azure.ai.vision as sdk
+
+            values["vision_service"] = sdk.VisionServiceOptions(
+                endpoint=azure_cogs_endpoint, key=azure_cogs_key
+            )
+
+            values["analysis_options"] = sdk.ImageAnalysisOptions()
+            values["analysis_options"].features = (
+                sdk.ImageAnalysisFeature.CAPTION
+                | sdk.ImageAnalysisFeature.OBJECTS
+                | sdk.ImageAnalysisFeature.TAGS
+                | sdk.ImageAnalysisFeature.TEXT
+            )
+        except ImportError:
+            raise ImportError(
+                "azure-ai-vision is not installed. "
+                "Run `pip install azure-ai-vision` to install."
+            )
+
+        return values
+
+    def _image_analysis(self, image_path: str) -> Dict:
+        try:
+            import azure.ai.vision as sdk
+        except ImportError:
+            pass
+
+        image_src_type = detect_file_src_type(image_path)
+        if image_src_type == "local":
+            vision_source = sdk.VisionSource(filename=image_path)
+        elif image_src_type == "remote":
+            vision_source = sdk.VisionSource(url=image_path)
+        else:
+            raise ValueError(f"Invalid image path: {image_path}")
+
+        image_analyzer = sdk.ImageAnalyzer(
+            self.vision_service, vision_source, self.analysis_options
+        )
+        result = image_analyzer.analyze()
+
+        res_dict = {}
+        if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:
+            if result.caption is not None:
+                res_dict["caption"] = result.caption.content
+
+            if result.objects is not None:
+                res_dict["objects"] = [obj.name for obj in result.objects]
+
+            if result.tags is not None:
+                res_dict["tags"] = [tag.name for tag in result.tags]
+
+            if result.text is not None:
+                res_dict["text"] = [line.content for line in result.text.lines]
+
+        else:
+            error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
+            raise RuntimeError(
+                f"Image analysis failed.\n"
+                f"Reason: {error_details.reason}\n"
+                f"Details: {error_details.message}"
+            )
+
+        return res_dict
+
+    def _format_image_analysis_result(self, image_analysis_result: Dict) -> str:
+        formatted_result = []
+        if "caption" in image_analysis_result:
+            formatted_result.append("Caption: " + image_analysis_result["caption"])
+
+        if (
+            "objects" in image_analysis_result
+            and len(image_analysis_result["objects"]) > 0
+        ):
+            formatted_result.append(
+                "Objects: " + ", ".join(image_analysis_result["objects"])
+            )
+
+        if "tags" in image_analysis_result and len(image_analysis_result["tags"]) > 0:
+            formatted_result.append("Tags: " + ", ".join(image_analysis_result["tags"]))
+
+        if "text" in image_analysis_result and len(image_analysis_result["text"]) > 0:
+            formatted_result.append("Text: " + ", ".join(image_analysis_result["text"]))
+
+        return "\n".join(formatted_result)
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        try:
+            image_analysis_result = self._image_analysis(query)
+            if not image_analysis_result:
+                return "No good image analysis result was found"
+
+            return self._format_image_analysis_result(image_analysis_result)
+        except Exception as e:
+            raise RuntimeError(f"Error while running AzureCogsImageAnalysisTool: {e}")
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError("AzureCogsImageAnalysisTool does not support async")

langchain/tools/azure_cognitive_services/speech2text.py

@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+import logging
+import time
+from typing import Any, Dict, Optional
+
+from pydantic import root_validator
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.tools.azure_cognitive_services.utils import (
+    detect_file_src_type,
+    download_audio_from_url,
+)
+from langchain.tools.base import BaseTool
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class AzureCogsSpeech2TextTool(BaseTool):
+    """Tool that queries the Azure Cognitive Services Speech2Text API.
+
+    In order to set this up, follow instructions at:
+    https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-speech-to-text?pivots=programming-language-python
+    """
+
+    azure_cogs_key: str = ""  #: :meta private:
+    azure_cogs_region: str = ""  #: :meta private:
+    speech_language: str = "en-US"  #: :meta private:
+    speech_config: Any  #: :meta private:
+
+    name = "Azure Cognitive Services Speech2Text"
+    description = (
+        "A wrapper around Azure Cognitive Services Speech2Text. "
+        "Useful for when you need to transcribe audio to text. "
+        "Input should be a url to an audio file."
+    )
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and endpoint exists in environment."""
+        azure_cogs_key = get_from_dict_or_env(
+            values, "azure_cogs_key", "AZURE_COGS_KEY"
+        )
+
+        azure_cogs_region = get_from_dict_or_env(
+            values, "azure_cogs_region", "AZURE_COGS_REGION"
+        )
+
+        try:
+            import azure.cognitiveservices.speech as speechsdk
+
+            values["speech_config"] = speechsdk.SpeechConfig(
+                subscription=azure_cogs_key, region=azure_cogs_region
+            )
+        except ImportError:
+            raise ImportError(
+                "azure-cognitiveservices-speech is not installed. "
+                "Run `pip install azure-cognitiveservices-speech` to install."
+            )
+
+        return values
+
+    def _continuous_recognize(self, speech_recognizer: Any) -> str:
+        done = False
+        text = ""
+
+        def stop_cb(evt: Any) -> None:
+            """callback that stop continuous recognition"""
+            speech_recognizer.stop_continuous_recognition_async()
+            nonlocal done
+            done = True
+
+        def retrieve_cb(evt: Any) -> None:
+            """callback that retrieves the intermediate recognition results"""
+            nonlocal text
+            text += evt.result.text
+
+        # retrieve text on recognized events
+        speech_recognizer.recognized.connect(retrieve_cb)
+        # stop continuous recognition on either session stopped or canceled events
+        speech_recognizer.session_stopped.connect(stop_cb)
+        speech_recognizer.canceled.connect(stop_cb)
+
+        # Start continuous speech recognition
+        speech_recognizer.start_continuous_recognition_async()
+        while not done:
+            time.sleep(0.5)
+        return text
+
+    def _speech2text(self, audio_path: str, speech_language: str) -> str:
+        try:
+            import azure.cognitiveservices.speech as speechsdk
+        except ImportError:
+            pass
+
+        audio_src_type = detect_file_src_type(audio_path)
+        if audio_src_type == "local":
+            audio_config = speechsdk.AudioConfig(filename=audio_path)
+        elif audio_src_type == "remote":
+            tmp_audio_path = download_audio_from_url(audio_path)
+            audio_config = speechsdk.AudioConfig(filename=tmp_audio_path)
+        else:
+            raise ValueError(f"Invalid audio path: {audio_path}")
+
+        self.speech_config.speech_recognition_language = speech_language
+        speech_recognizer = speechsdk.SpeechRecognizer(self.speech_config, audio_config)
+        return self._continuous_recognize(speech_recognizer)
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        try:
+            text = self._speech2text(query, self.speech_language)
+            return text
+        except Exception as e:
+            raise RuntimeError(f"Error while running AzureCogsSpeech2TextTool: {e}")
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError("AzureCogsSpeech2TextTool does not support async")

langchain/tools/azure_cognitive_services/text2speech.py

@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+import logging
+import tempfile
+from typing import Any, Dict, Optional
+
+from pydantic import root_validator
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.tools.base import BaseTool
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class AzureCogsText2SpeechTool(BaseTool):
+    """Tool that queries the Azure Cognitive Services Text2Speech API.
+
+    In order to set this up, follow instructions at:
+    https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-text-to-speech?pivots=programming-language-python
+    """
+
+    azure_cogs_key: str = ""  #: :meta private:
+    azure_cogs_region: str = ""  #: :meta private:
+    speech_language: str = "en-US"  #: :meta private:
+    speech_config: Any  #: :meta private:
+
+    name = "Azure Cognitive Services Text2Speech"
+    description = (
+        "A wrapper around Azure Cognitive Services Text2Speech. "
+        "Useful for when you need to convert text to speech. "
+    )
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and endpoint exists in environment."""
+        azure_cogs_key = get_from_dict_or_env(
+            values, "azure_cogs_key", "AZURE_COGS_KEY"
+        )
+
+        azure_cogs_region = get_from_dict_or_env(
+            values, "azure_cogs_region", "AZURE_COGS_REGION"
+        )
+
+        try:
+            import azure.cognitiveservices.speech as speechsdk
+
+            values["speech_config"] = speechsdk.SpeechConfig(
+                subscription=azure_cogs_key, region=azure_cogs_region
+            )
+        except ImportError:
+            raise ImportError(
+                "azure-cognitiveservices-speech is not installed. "
+                "Run `pip install azure-cognitiveservices-speech` to install."
+            )
+
+        return values
+
+    def _text2speech(self, text: str, speech_language: str) -> str:
+        try:
+            import azure.cognitiveservices.speech as speechsdk
+        except ImportError:
+            pass
+
+        self.speech_config.speech_synthesis_language = speech_language
+        speech_synthesizer = speechsdk.SpeechSynthesizer(
+            speech_config=self.speech_config, audio_config=None
+        )
+        result = speech_synthesizer.speak_text(text)
+
+        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
+            stream = speechsdk.AudioDataStream(result)
+            with tempfile.NamedTemporaryFile(
+                mode="wb", suffix=".wav", delete=False
+            ) as f:
+                stream.save_to_wav_file(f.name)
+
+            return f.name
+
+        elif result.reason == speechsdk.ResultReason.Canceled:
+            cancellation_details = result.cancellation_details
+            logger.debug(f"Speech synthesis canceled: {cancellation_details.reason}")
+            if cancellation_details.reason == speechsdk.CancellationReason.Error:
+                raise RuntimeError(
+                    f"Speech synthesis error: {cancellation_details.error_details}"
+                )
+
+            return "Speech synthesis canceled."
+
+        else:
+            return f"Speech synthesis failed: {result.reason}"
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        try:
+            speech_file = self._text2speech(query, self.speech_language)
+            return speech_file
+        except Exception as e:
+            raise RuntimeError(f"Error while running AzureCogsText2SpeechTool: {e}")
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError("AzureCogsText2SpeechTool does not support async")