Fix linting

The awesome JIRA tool created by @zywilliamli calls the `create_issue()`
method to create issues, however, the actual method is `issue_create()`.

Details in the Documentation here:
https://atlassian-python-api.readthedocs.io/jira.html#manage-issues

docs/ecosystem/yeagerai.md

@@ -0,0 +1,43 @@
+# Yeager.ai
+
+This page covers how to use [Yeager.ai](https://yeager.ai) to generate LangChain tools and agents.
+
+## What is Yeager.ai?
+Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. 
+
+It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications.
+
+## yAgents
+Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. 
+
+### How to use?
+```
+pip install yeagerai-agent
+yeagerai-agent
+```
+Go to http://127.0.0.1:7860
+
+This will install the necessary dependencies and set up yAgents on your system. After the first run, yAgents will create a .env file where you can input your OpenAI API key. You can do the same directly from the Gradio interface under the tab "Settings".
+
+`OPENAI_API_KEY=<your_openai_api_key_here>`
+
+We recommend using GPT-4,. However, the tool can also work with GPT-3 if the problem is broken down sufficiently.
+
+### Creating and Executing Tools with yAgents
+yAgents makes it easy to create and execute AI-powered tools. Here's a brief overview of the process:
+1. Create a tool: To create a tool, provide a natural language prompt to yAgents. The prompt should clearly describe the tool's purpose and functionality. For example:
+`create a tool that returns the n-th prime number`
+
+2. Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example:
+`load the tool that you just created it into your toolkit`
+
+3. Execute the tool: To run a tool or agent, simply provide a command to yAgents that includes the name of the tool and any required parameters. For example:
+`generate the 50th prime number`
+
+You can see a video of how it works [here](https://www.youtube.com/watch?v=KA5hCM3RaWE).
+
+As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity.
+
+For more information, see [yAgents' Github](https://github.com/yeagerai/yeagerai-agent) or our [docs](https://yeagerai.gitbook.io/docs/general/welcome-to-yeager.ai) 
+
+

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

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "019d8520",
    "metadata": {},
    "outputs": [],
@@ -128,10 +128,69 @@
     "len(docs)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "598a2805",
+   "metadata": {},
+   "source": [
+    "If you need to load Python source code files, use the `PythonLoader`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c558bd73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import PythonLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a3cfaba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DirectoryLoader('../../../../../', glob=\"**/*.py\", loader_cls=PythonLoader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e2e1e26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ffb8ff36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "691"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(docs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "984c8429",
+   "id": "7f6e0eae",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -153,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,

docs/modules/indexes/retrievers/examples/contextual-compression.ipynb

@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fc0db1bc",
+   "metadata": {},
+   "source": [
+    "# Contextual Compression Retriever\n",
+    "\n",
+    "This notebook introduces the concept of DocumentCompressors and the ContextualCompressionRetriever. The core idea is simple: given a specific query, we should be able to return only the documents relevant to that query, and only the parts of those documents that are relevant. The ContextualCompressionsRetriever is a wrapper for another retriever that iterates over the initial output of the base retriever and filters and compresses those initial documents, so that only the most relevant information is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28e8dc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper function for printing docs\n",
+    "\n",
+    "def pretty_print_docs(docs):\n",
+    "    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fa3d916",
+   "metadata": {},
+   "source": [
+    "## Using a vanilla vector store retriever\n",
+    "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can see that given an example question our retriever returns one or two relevant docs and a few irrelevant docs. And even the relevant docs have a lot of irrelevant information in them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9fbcc58f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "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. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "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.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
+      "\n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
+      "\n",
+      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
+      "\n",
+      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
+      "\n",
+      "First, beat the opioid epidemic.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 4:\n",
+      "\n",
+      "Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n",
+      "\n",
+      "And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \n",
+      "\n",
+      "That ends on my watch. \n",
+      "\n",
+      "Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n",
+      "\n",
+      "We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n",
+      "\n",
+      "Let’s pass the Paycheck Fairness Act and paid leave.  \n",
+      "\n",
+      "Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n",
+      "\n",
+      "Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_documents(documents)\n",
+    "retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()\n",
+    "\n",
+    "docs = retriever.get_relevant_documents(\"What did the president say about Ketanji Brown Jackson\")\n",
+    "pretty_print_docs(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7648612",
+   "metadata": {},
+   "source": [
+    "## Adding contextual compression with an `LLMChainExtractor`\n",
+    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `LLMChainExtractor`, which will iterate over the initially returned documents and extract from each only the content that is relevant to the query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a658023",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "\"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "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.\"\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "\"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers import ContextualCompressionRetriever\n",
+    "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "compressor = LLMChainExtractor.from_llm(llm)\n",
+    "compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)\n",
+    "\n",
+    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cd38f3a",
+   "metadata": {},
+   "source": [
+    "## More built-in compressors: filters\n",
+    "### `LLMChainFilter`\n",
+    "The `LLMChainFilter` is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b216a767",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "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. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "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.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.retrievers.document_compressors import LLMChainFilter\n",
+    "\n",
+    "_filter = LLMChainFilter.from_llm(llm)\n",
+    "compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)\n",
+    "\n",
+    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c709598",
+   "metadata": {},
+   "source": [
+    "### `EmbeddingsFilter`\n",
+    "\n",
+    "Making an extra LLM call over each retrieved document is expensive and slow. The `EmbeddingsFilter` provides a cheaper and faster option by embedding the documents and query and only returning those documents which have sufficiently similar embeddings to the query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6fbc801f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "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. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "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.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
+      "\n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
+      "\n",
+      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
+      "\n",
+      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
+      "\n",
+      "First, beat the opioid epidemic.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
+    "compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)\n",
+    "\n",
+    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07365d36",
+   "metadata": {},
+   "source": [
+    "# Stringing compressors and document transformers together\n",
+    "Using the `DocumentCompressorPipeline` we can also easily combine multiple compressors in sequence. Along with compressors we can add `BaseDocumentTransformer`s to our pipeline, which don't perform any contextual compression but simply perform some transformation on a set of documents. For example `TextSplitter`s can be used as document transformers to split documents into smaller pieces, and the `EmbeddingsRedundantFilter` can be used to filter out redundant documents based on embedding similarity between documents.\n",
+    "\n",
+    "Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2a150a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_transformers import EmbeddingsRedundantFilter\n",
+    "from langchain.retrievers.document_compressors import DocumentCompressorPipeline\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "\n",
+    "splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=\". \")\n",
+    "redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)\n",
+    "relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
+    "pipeline_compressor = DocumentCompressorPipeline(\n",
+    "    transformers=[splitter, redundant_filter, relevant_filter]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3ceab64a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder\n"
+     ]
+    }
+   ],
+   "source": [
+    "compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)\n",
+    "\n",
+    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cfd9fc5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
@@ -35,7 +36,7 @@
     "               id bigint,\n",
     "               content text,\n",
     "               metadata jsonb,\n",
-    "               -- we return matched vectors to allow to execute maximal marginal relevance searches\n",
+    "               -- we return matched vectors to enable maximal marginal relevance searches\n",
     "               embedding vector(1536),\n",
     "               similarity float)\n",
     "           LANGUAGE plpgsql\n",
@@ -48,11 +49,11 @@
     "               content,\n",
     "               metadata,\n",
     "               embedding,\n",
-    "               1 -(docstore.embedding <=> query_embedding) AS similarity\n",
+    "               1 -(documents.embedding <=> query_embedding) AS similarity\n",
     "           FROM\n",
-    "               docstore\n",
+    "               documents\n",
     "           ORDER BY\n",
-    "               docstore.embedding <=> query_embedding\n",
+    "               documents.embedding <=> query_embedding\n",
     "           LIMIT match_count;\n",
     "       END;\n",
     "       $$;\n",
@@ -390,7 +391,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/reference.rst

@@ -7,8 +7,10 @@ Full documentation on all methods, classes, and APIs in LangChain.
 .. toctree::
    :maxdepth: 1
 
+   ./reference/models.rst
    ./reference/prompts.rst
-   LLMs<./reference/modules/llms>
-   ./reference/utils.rst
-   Chains<./reference/modules/chains>
-   Agents<./reference/modules/agents>
+   ./reference/indexes.rst
+   ./reference/modules/memory.rst
+   ./reference/modules/chains.rst
+   ./reference/agents.rst
+   ./reference/modules/utilities.rst

docs/reference/agents.rst

@@ -0,0 +1,12 @@
+Agents
+==============
+
+Reference guide for Agents and associated abstractions.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   modules/agents
+   modules/tools
+   modules/agent_toolkits

docs/reference/indexes.rst

@@ -0,0 +1,16 @@
+Indexes
+==============
+Indexes refer to ways to structure documents so that LLMs can best interact with them.
+LangChain has a number of modules that help you load, structure, store, and retrieve documents.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   modules/docstore
+   modules/text_splitter
+   modules/document_loaders
+   modules/vectorstores
+   modules/retrievers
+   modules/document_compressors
+   modules/document_transformers

docs/reference/models.rst

@@ -0,0 +1,12 @@
+Models
+==============
+
+LangChain provides interfaces and integrations for a number of different types of models.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   modules/llms
+   modules/chat_models
+   modules/embeddings

docs/reference/modules/agent_toolkits.rst

@@ -0,0 +1,7 @@
+Agent Toolkits
+===============================
+
+.. automodule:: langchain.agents.agent_toolkits
+   :members:
+   :undoc-members:
+

docs/reference/modules/chat_models.rst

@@ -0,0 +1,7 @@
+Chat Models
+===============================
+
+.. automodule:: langchain.chat_models
+   :members:
+   :undoc-members:
+

docs/reference/modules/document_compressors.rst

@@ -0,0 +1,7 @@
+Document Compressors
+===============================
+
+.. automodule:: langchain.retrievers.document_compressors
+   :members:
+   :undoc-members:
+

docs/reference/modules/document_loaders.rst

@@ -0,0 +1,7 @@
+Document Loaders
+===============================
+
+.. automodule:: langchain.document_loaders
+   :members:
+   :undoc-members:
+

docs/reference/modules/document_transformers.rst

@@ -0,0 +1,7 @@
+Document Transformers
+===============================
+
+.. automodule:: langchain.document_transformers
+   :members:
+   :undoc-members:
+

docs/reference/modules/memory.rst

@@ -0,0 +1,7 @@
+Memory
+===============================
+
+.. automodule:: langchain.memory
+   :members:
+   :undoc-members:
+

docs/reference/modules/output_parsers.rst

@@ -0,0 +1,7 @@
+Output Parsers
+===============================
+
+.. automodule:: langchain.output_parsers
+   :members:
+   :undoc-members:
+

docs/reference/modules/retrievers.rst

@@ -0,0 +1,7 @@
+Retrievers
+===============================
+
+.. automodule:: langchain.retrievers
+   :members:
+   :undoc-members:
+

docs/reference/modules/tools.rst

@@ -0,0 +1,7 @@
+Tools
+===============================
+
+.. automodule:: langchain.tools
+   :members:
+   :undoc-members:
+

docs/reference/modules/utilities.rst

@@ -0,0 +1,7 @@
+Utilities
+===============================
+
+.. automodule:: langchain.utilities
+   :members:
+   :undoc-members:
+

docs/reference/modules/vectorstores.rst

@@ -1,4 +1,4 @@
-VectorStores
+Vector Stores
 =============================
 
 .. automodule:: langchain.vectorstores

docs/reference/prompts.rst

@@ -7,5 +7,6 @@ The reference guides here all relate to objects for working with Prompts.
    :maxdepth: 1
    :glob:
 
-   modules/prompt
+   modules/prompts
    modules/example_selector
+   modules/output_parsers

docs/reference/utils.rst

@@ -1,27 +0,0 @@
-Utilities
-==============
-
-There are a lot of different utilities that LangChain provides integrations for
-These guides go over how to use them.
-These can largely be grouped into two categories: generic utilities, and then utilities for working with larger text documents.
-
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-   :caption: Generic Utilities
-
-   modules/python
-   modules/serpapi
-   modules/searx_search
-
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-   :caption: Utilities for working with Documents
-
-   modules/docstore
-   modules/text_splitter
-   modules/embeddings
-   modules/vectorstore

docs/use_cases/personal_assistants.md

@@ -20,3 +20,4 @@ Highlighting specific parts:
 Specific examples of this include:
 
 - [AI Plugins](agents/custom_agent_with_plugin_retrieval.ipynb): an implementation of an agent that is designed to be able to use all AI Plugins.
+- [Wikibase Agent](agents/wikibase_agent.ipynb): an implementation of an agent that is designed to interact with Wikibase.

docs/youtube.md

@@ -1,9 +1,11 @@
 # YouTube
 
-This is a collection of LangChain tutorials and videos.
+This is a collection of `LangChain` tutorials and videos on `YouTube`.
 
-### Introduction to LangChain with Harrison Chase creator of LangChain
-- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
+### Introduction to LangChain with Harrison Chase, creator of LangChain
+- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
+- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
+- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
 
 ## Tutorials
 
@@ -81,7 +83,6 @@ This is a collection of LangChain tutorials and videos.
 - [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
 - [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
 - [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
-- [Building the Future with LLMs, LangChain, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
 - [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
 - [Analyze Custom `CSV` Data with `GPT-4` using Langchain](https://youtu.be/Ew3sGdX8at4) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov) 
 - [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)

langchain/callbacks/comet_ml_callback.py

@@ -130,7 +130,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         warning = (
             "The comet_ml callback is currently in beta and is subject to change "
             "based on updates to `langchain`. Please report any issues to "
-            "https://github.com/comet-ml/issue_tracking/issues with the tag "
+            "https://github.com/comet-ml/issue-tracking/issues with the tag "
             "`langchain`."
         )
         self.comet_ml.LOGGER.warning(warning)

langchain/chains/conversational_retrieval/base.py

@@ -15,16 +15,32 @@ from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, BaseRetriever, Document
+from langchain.schema import BaseLanguageModel, BaseMessage, BaseRetriever, Document
 from langchain.vectorstores.base import VectorStore
 
+# Depending on the memory type and configuration, the chat history format may differ.
+# This needs to be consolidated.
+CHAT_TURN_TYPE = Union[Tuple[str, str], BaseMessage]
 
-def _get_chat_history(chat_history: List[Tuple[str, str]]) -> str:
+
+_ROLE_MAP = {"human": "Human: ", "ai": "Assistant: "}
+
+
+def _get_chat_history(chat_history: List[CHAT_TURN_TYPE]) -> str:
     buffer = ""
-    for human_s, ai_s in chat_history:
-        human = "Human: " + human_s
-        ai = "Assistant: " + ai_s
-        buffer += "\n" + "\n".join([human, ai])
+    for dialogue_turn in chat_history:
+        if isinstance(dialogue_turn, BaseMessage):
+            role_prefix = _ROLE_MAP.get(dialogue_turn.type, f"{dialogue_turn.type}: ")
+            buffer += f"\n{role_prefix}{dialogue_turn.content}"
+        elif isinstance(dialogue_turn, tuple):
+            human = "Human: " + dialogue_turn[0]
+            ai = "Assistant: " + dialogue_turn[1]
+            buffer += "\n" + "\n".join([human, ai])
+        else:
+            raise ValueError(
+                f"Unsupported chat history format: {type(dialogue_turn)}."
+                f" Full chat history: {chat_history} "
+            )
     return buffer
 
 
@@ -35,7 +51,7 @@ class BaseConversationalRetrievalChain(Chain):
     question_generator: LLMChain
     output_key: str = "answer"
     return_source_documents: bool = False
-    get_chat_history: Optional[Callable[[Tuple[str, str]], str]] = None
+    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
     """Return the source documents."""
 
     class Config:

langchain/chains/sql_database/prompt.py

@@ -40,6 +40,27 @@ DECIDER_PROMPT = PromptTemplate(
     output_parser=CommaSeparatedListOutputParser(),
 )
 
+_duckdb_prompt = """You are a DuckDB expert. Given an input question, first create a syntactically correct DuckDB query to run, then look at the results of the query and return the answer to the input question.
+Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per DuckDB. You can order the results to return the most informative data in the database.
+Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
+Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
+
+Use the following format:
+
+Question: "Question here"
+SQLQuery: "SQL Query to run"
+SQLResult: "Result of the SQLQuery"
+Answer: "Final answer here"
+
+Only use the following tables:
+{table_info}
+
+Question: {input}"""
+
+DUCKDB_PROMPT = PromptTemplate(
+    input_variables=["input", "table_info", "top_k"],
+    template=_duckdb_prompt,
+)
 
 _googlesql_prompt = """You are a GoogleSQL expert. Given an input question, first create a syntactically correct GoogleSQL query to run, then look at the results of the query and return the answer to the input question.
 Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per GoogleSQL. You can order the results to return the most informative data in the database.
@@ -201,6 +222,7 @@ SQLITE_PROMPT = PromptTemplate(
 
 
 SQL_PROMPTS = {
+    "duckdb": DUCKDB_PROMPT,
     "googlesql": GOOGLESQL_PROMPT,
     "mssql": MSSQL_PROMPT,
     "mysql": MYSQL_PROMPT,

langchain/document_loaders/__init__.py

@@ -55,8 +55,10 @@ from langchain.document_loaders.pdf import (
     UnstructuredPDFLoader,
 )
 from langchain.document_loaders.powerpoint import UnstructuredPowerPointLoader
+from langchain.document_loaders.python import PythonLoader
 from langchain.document_loaders.readthedocs import ReadTheDocsLoader
 from langchain.document_loaders.roam import RoamLoader
+from langchain.document_loaders.rtf import UnstructuredRTFLoader
 from langchain.document_loaders.s3_directory import S3DirectoryLoader
 from langchain.document_loaders.s3_file import S3FileLoader
 from langchain.document_loaders.sitemap import SitemapLoader
@@ -106,6 +108,7 @@ __all__ = [
     "OutlookMessageLoader",
     "UnstructuredEPubLoader",
     "UnstructuredMarkdownLoader",
+    "UnstructuredRTFLoader",
     "RoamLoader",
     "YoutubeLoader",
     "S3FileLoader",
@@ -154,4 +157,5 @@ __all__ = [
     "ImageCaptionLoader",
     "DiscordChatLoader",
     "ConfluenceLoader",
+    "PythonLoader",
 ]

langchain/document_loaders/html_bs.py

@@ -24,7 +24,8 @@ class BSHTMLLoader(BaseLoader):
             import bs4  # noqa:F401
         except ImportError:
             raise ValueError(
-                "bs4 package not found, please install it with " "`pip install bs4`"
+                "beautifulsoup4 package not found, please install it with "
+                "`pip install beautifulsoup4`"
             )
 
         self.file_path = file_path

langchain/document_loaders/python.py

@@ -0,0 +1,14 @@
+import tokenize
+
+from langchain.document_loaders.text import TextLoader
+
+
+class PythonLoader(TextLoader):
+    """
+    Load Python files, respecting any non-default encoding if specified.
+    """
+
+    def __init__(self, file_path: str):
+        with open(file_path, "rb") as f:
+            encoding, _ = tokenize.detect_encoding(f.readline)
+        super().__init__(file_path=file_path, encoding=encoding)

langchain/document_loaders/rtf.py

@@ -0,0 +1,28 @@
+"""Loader that loads rich text files."""
+from typing import Any, List
+
+from langchain.document_loaders.unstructured import (
+    UnstructuredFileLoader,
+    satisfies_min_unstructured_version,
+)
+
+
+class UnstructuredRTFLoader(UnstructuredFileLoader):
+    """Loader that uses unstructured to load rtf files."""
+
+    def __init__(
+        self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
+    ):
+        min_unstructured_version = "0.5.12"
+        if not satisfies_min_unstructured_version(min_unstructured_version):
+            raise ValueError(
+                "Partitioning rtf files is only supported in "
+                f"unstructured>={min_unstructured_version}."
+            )
+
+        super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
+
+    def _get_elements(self) -> List:
+        from unstructured.partition.rtf import partition_rtf
+
+        return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)

langchain/document_loaders/youtube.py

@@ -114,7 +114,11 @@ class YoutubeLoader(BaseLoader):
     def load(self) -> List[Document]:
         """Load documents."""
         try:
-            from youtube_transcript_api import NoTranscriptFound, YouTubeTranscriptApi
+            from youtube_transcript_api import (
+                NoTranscriptFound,
+                TranscriptsDisabled,
+                YouTubeTranscriptApi,
+            )
         except ImportError:
             raise ImportError(
                 "Could not import youtube_transcript_api python package. "
@@ -129,7 +133,11 @@ class YoutubeLoader(BaseLoader):
             video_info = self._get_video_info()
             metadata.update(video_info)
 
-        transcript_list = YouTubeTranscriptApi.list_transcripts(self.video_id)
+        try:
+            transcript_list = YouTubeTranscriptApi.list_transcripts(self.video_id)
+        except TranscriptsDisabled:
+            return []
+
         try:
             transcript = transcript_list.find_transcript([self.language])
         except NoTranscriptFound:

langchain/document_transformers.py

@@ -0,0 +1,100 @@
+"""Transform documents"""
+from typing import Any, Callable, List, Sequence
+
+import numpy as np
+from pydantic import BaseModel, Field
+
+from langchain.embeddings.base import Embeddings
+from langchain.math_utils import cosine_similarity
+from langchain.schema import BaseDocumentTransformer, Document
+
+
+class _DocumentWithState(Document):
+    """Wrapper for a document that includes arbitrary state."""
+
+    state: dict = Field(default_factory=dict)
+    """State associated with the document."""
+
+    def to_document(self) -> Document:
+        """Convert the DocumentWithState to a Document."""
+        return Document(page_content=self.page_content, metadata=self.metadata)
+
+    @classmethod
+    def from_document(cls, doc: Document) -> "_DocumentWithState":
+        """Create a DocumentWithState from a Document."""
+        if isinstance(doc, cls):
+            return doc
+        return cls(page_content=doc.page_content, metadata=doc.metadata)
+
+
+def get_stateful_documents(
+    documents: Sequence[Document],
+) -> Sequence[_DocumentWithState]:
+    return [_DocumentWithState.from_document(doc) for doc in documents]
+
+
+def _filter_similar_embeddings(
+    embedded_documents: List[List[float]], similarity_fn: Callable, threshold: float
+) -> List[int]:
+    """Filter redundant documents based on the similarity of their embeddings."""
+    similarity = np.tril(similarity_fn(embedded_documents, embedded_documents), k=-1)
+    redundant = np.where(similarity > threshold)
+    redundant_stacked = np.column_stack(redundant)
+    redundant_sorted = np.argsort(similarity[redundant])[::-1]
+    included_idxs = set(range(len(embedded_documents)))
+    for first_idx, second_idx in redundant_stacked[redundant_sorted]:
+        if first_idx in included_idxs and second_idx in included_idxs:
+            # Default to dropping the second document of any highly similar pair.
+            included_idxs.remove(second_idx)
+    return list(sorted(included_idxs))
+
+
+def _get_embeddings_from_stateful_docs(
+    embeddings: Embeddings, documents: Sequence[_DocumentWithState]
+) -> List[List[float]]:
+    if len(documents) and "embedded_doc" in documents[0].state:
+        embedded_documents = [doc.state["embedded_doc"] for doc in documents]
+    else:
+        embedded_documents = embeddings.embed_documents(
+            [d.page_content for d in documents]
+        )
+        for doc, embedding in zip(documents, embedded_documents):
+            doc.state["embedded_doc"] = embedding
+    return embedded_documents
+
+
+class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
+    """Filter that drops redundant documents by comparing their embeddings."""
+
+    embeddings: Embeddings
+    """Embeddings to use for embedding document contents."""
+    similarity_fn: Callable = cosine_similarity
+    """Similarity function for comparing documents. Function expected to take as input
+    two matrices (List[List[float]]) and return a matrix of scores where higher values
+    indicate greater similarity."""
+    similarity_threshold: float = 0.95
+    """Threshold for determining when two documents are similar enough
+    to be considered redundant."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    def transform_documents(
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
+        """Filter down documents."""
+        stateful_documents = get_stateful_documents(documents)
+        embedded_documents = _get_embeddings_from_stateful_docs(
+            self.embeddings, stateful_documents
+        )
+        included_idxs = _filter_similar_embeddings(
+            embedded_documents, self.similarity_fn, self.similarity_threshold
+        )
+        return [stateful_documents[i] for i in sorted(included_idxs)]
+
+    async def atransform_documents(
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
+        raise NotImplementedError

langchain/embeddings/huggingface.py

@@ -1,7 +1,7 @@
 """Wrapper around HuggingFace embedding models."""
-from typing import Any, List, Optional
+from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import BaseModel, Extra, Field
 
 from langchain.embeddings.base import Embeddings
 
@@ -22,8 +22,10 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain.embeddings import HuggingFaceEmbeddings
+
             model_name = "sentence-transformers/all-mpnet-base-v2"
-            hf = HuggingFaceEmbeddings(model_name=model_name)
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
     """
 
     client: Any  #: :meta private:
@@ -32,6 +34,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     cache_folder: Optional[str] = None
     """Path to store models. 
     Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Key word arguments to pass to the model."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -40,7 +44,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             import sentence_transformers
 
             self.client = sentence_transformers.SentenceTransformer(
-                self.model_name, self.cache_folder
+                self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
             )
         except ImportError:
             raise ValueError(
@@ -90,13 +94,22 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain.embeddings import HuggingFaceInstructEmbeddings
+
             model_name = "hkunlp/instructor-large"
-            hf = HuggingFaceInstructEmbeddings(model_name=model_name)
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceInstructEmbeddings(
+                model_name=model_name, model_kwargs=model_kwargs
+            )
     """
 
     client: Any  #: :meta private:
     model_name: str = DEFAULT_INSTRUCT_MODEL
     """Model name to use."""
+    cache_folder: Optional[str] = None
+    """Path to store models. 
+    Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Key word arguments to pass to the model."""
     embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
     """Instruction to use for embedding documents."""
     query_instruction: str = DEFAULT_QUERY_INSTRUCTION
@@ -108,7 +121,9 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
         try:
             from InstructorEmbedding import INSTRUCTOR
 
-            self.client = INSTRUCTOR(self.model_name)
+            self.client = INSTRUCTOR(
+                self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
+            )
         except ImportError as e:
             raise ValueError("Dependencies for InstructorEmbedding not found.") from e
 

langchain/experimental/autonomous_agents/autogpt/output_parser.py

@@ -44,7 +44,8 @@ class AutoGPTOutputParser(BaseAutoGPTOutputParser):
                 name=parsed["command"]["name"],
                 args=parsed["command"]["args"],
             )
-        except KeyError:
+        except (KeyError, TypeError):
+            # If the command is null or incomplete, return an erroneous tool
             return AutoGPTAction(
                 name="ERROR", args={"error": f"Incomplete command args: {parsed}"}
             )

langchain/llms/openai.py

@@ -217,12 +217,8 @@ class BaseOpenAI(BaseLLM):
 
             openai.api_key = openai_api_key
             if openai_api_base:
-                print("USING API_BASE: ")
-                print(openai_api_base)
                 openai.api_base = openai_api_base
             if openai_organization:
-                print("USING ORGANIZATION: ")
-                print(openai_organization)
                 openai.organization = openai_organization
             values["client"] = openai.Completion
         except ImportError:

langchain/math_utils.py

@@ -0,0 +1,22 @@
+"""Math utils."""
+from typing import List, Union
+
+import numpy as np
+
+Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
+
+
+def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
+    """Row-wise cosine similarity between two equal-width matrices."""
+    if len(X) == 0 or len(Y) == 0:
+        return np.array([])
+    X = np.array(X)
+    Y = np.array(Y)
+    if X.shape[1] != Y.shape[1]:
+        raise ValueError("Number of columns in X and Y must be the same.")
+
+    X_norm = np.linalg.norm(X, axis=1)
+    Y_norm = np.linalg.norm(Y, axis=1)
+    similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
+    similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
+    return similarity

langchain/output_parsers/boolean.py

@@ -0,0 +1,29 @@
+from langchain.schema import BaseOutputParser
+
+
+class BooleanOutputParser(BaseOutputParser[bool]):
+    true_val: str = "YES"
+    false_val: str = "NO"
+
+    def parse(self, text: str) -> bool:
+        """Parse the output of an LLM call to a boolean.
+
+        Args:
+            text: output of language model
+
+        Returns:
+            boolean
+
+        """
+        cleaned_text = text.strip()
+        if cleaned_text not in (self.true_val, self.false_val):
+            raise ValueError(
+                f"BooleanOutputParser expected output value to either be "
+                f"{self.true_val} or {self.false_val}. Received {cleaned_text}."
+            )
+        return cleaned_text == self.true_val
+
+    @property
+    def _type(self) -> str:
+        """Snake-case string identifier for output parser type."""
+        return "boolean_output_parser"

langchain/retrievers/__init__.py

@@ -1,4 +1,5 @@
 from langchain.retrievers.chatgpt_plugin_retriever import ChatGPTPluginRetriever
+from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain.retrievers.databerry import DataberryRetriever
 from langchain.retrievers.elastic_search_bm25 import ElasticSearchBM25Retriever
 from langchain.retrievers.metal import MetalRetriever
@@ -13,6 +14,7 @@ from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetr
 
 __all__ = [
     "ChatGPTPluginRetriever",
+    "ContextualCompressionRetriever",
     "RemoteLangChainRetriever",
     "PineconeHybridSearchRetriever",
     "MetalRetriever",

langchain/retrievers/contextual_compression.py

@@ -0,0 +1,51 @@
+"""Retriever that wraps a base retriever and filters the results."""
+from typing import List
+
+from pydantic import BaseModel, Extra
+
+from langchain.retrievers.document_compressors.base import (
+    BaseDocumentCompressor,
+)
+from langchain.schema import BaseRetriever, Document
+
+
+class ContextualCompressionRetriever(BaseRetriever, BaseModel):
+    """Retriever that wraps a base retriever and compresses the results."""
+
+    base_compressor: BaseDocumentCompressor
+    """Compressor for compressing retrieved documents."""
+
+    base_retriever: BaseRetriever
+    """Base Retriever to use for getting relevant documents."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    def get_relevant_documents(self, query: str) -> List[Document]:
+        """Get documents relevant for a query.
+
+        Args:
+            query: string to find relevant documents for
+
+        Returns:
+            Sequence of relevant documents
+        """
+        docs = self.base_retriever.get_relevant_documents(query)
+        compressed_docs = self.base_compressor.compress_documents(docs, query)
+        return list(compressed_docs)
+
+    async def aget_relevant_documents(self, query: str) -> List[Document]:
+        """Get documents relevant for a query.
+
+        Args:
+            query: string to find relevant documents for
+
+        Returns:
+            List of relevant documents
+        """
+        docs = await self.base_retriever.aget_relevant_documents(query)
+        compressed_docs = await self.base_compressor.acompress_documents(docs, query)
+        return list(compressed_docs)

langchain/retrievers/document_compressors/__init__.py

@@ -0,0 +1,17 @@
+from langchain.retrievers.document_compressors.base import DocumentCompressorPipeline
+from langchain.retrievers.document_compressors.chain_extract import (
+    LLMChainExtractor,
+)
+from langchain.retrievers.document_compressors.chain_filter import (
+    LLMChainFilter,
+)
+from langchain.retrievers.document_compressors.embeddings_filter import (
+    EmbeddingsFilter,
+)
+
+__all__ = [
+    "DocumentCompressorPipeline",
+    "EmbeddingsFilter",
+    "LLMChainExtractor",
+    "LLMChainFilter",
+]

langchain/retrievers/document_compressors/base.py

@@ -0,0 +1,61 @@
+"""Interface for retrieved document compressors."""
+from abc import ABC, abstractmethod
+from typing import List, Sequence, Union
+
+from pydantic import BaseModel
+
+from langchain.schema import BaseDocumentTransformer, Document
+
+
+class BaseDocumentCompressor(BaseModel, ABC):
+    """"""
+
+    @abstractmethod
+    def compress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Compress retrieved documents given the query context."""
+
+    @abstractmethod
+    async def acompress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Compress retrieved documents given the query context."""
+
+
+class DocumentCompressorPipeline(BaseDocumentCompressor):
+    """Document compressor that uses a pipeline of transformers."""
+
+    transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]]
+    """List of document filters that are chained together and run in sequence."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    def compress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Transform a list of documents."""
+        for _transformer in self.transformers:
+            if isinstance(_transformer, BaseDocumentCompressor):
+                documents = _transformer.compress_documents(documents, query)
+            elif isinstance(_transformer, BaseDocumentTransformer):
+                documents = _transformer.transform_documents(documents)
+            else:
+                raise ValueError(f"Got unexpected transformer type: {_transformer}")
+        return documents
+
+    async def acompress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Compress retrieved documents given the query context."""
+        for _transformer in self.transformers:
+            if isinstance(_transformer, BaseDocumentCompressor):
+                documents = await _transformer.acompress_documents(documents, query)
+            elif isinstance(_transformer, BaseDocumentTransformer):
+                documents = await _transformer.atransform_documents(documents)
+            else:
+                raise ValueError(f"Got unexpected transformer type: {_transformer}")
+        return documents

langchain/retrievers/document_compressors/chain_extract.py

@@ -0,0 +1,77 @@
+"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
+from typing import Any, Callable, Dict, Optional, Sequence
+
+from langchain import LLMChain, PromptTemplate
+from langchain.retrievers.document_compressors.base import (
+    BaseDocumentCompressor,
+)
+from langchain.retrievers.document_compressors.chain_extract_prompt import (
+    prompt_template,
+)
+from langchain.schema import BaseLanguageModel, BaseOutputParser, Document
+
+
+def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
+    """Return the compression chain input."""
+    return {"question": query, "context": doc.page_content}
+
+
+class NoOutputParser(BaseOutputParser[str]):
+    """Parse outputs that could return a null string of some sort."""
+
+    no_output_str: str = "NO_OUTPUT"
+
+    def parse(self, text: str) -> str:
+        cleaned_text = text.strip()
+        if cleaned_text == self.no_output_str:
+            return ""
+        return cleaned_text
+
+
+def _get_default_chain_prompt() -> PromptTemplate:
+    output_parser = NoOutputParser()
+    template = prompt_template.format(no_output_str=output_parser.no_output_str)
+    return PromptTemplate(
+        template=template,
+        input_variables=["question", "context"],
+        output_parser=output_parser,
+    )
+
+
+class LLMChainExtractor(BaseDocumentCompressor):
+    llm_chain: LLMChain
+    """LLM wrapper to use for compressing documents."""
+
+    get_input: Callable[[str, Document], dict] = default_get_input
+    """Callable for constructing the chain input from the query and a Document."""
+
+    def compress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Compress page content of raw documents."""
+        compressed_docs = []
+        for doc in documents:
+            _input = self.get_input(query, doc)
+            output = self.llm_chain.predict_and_parse(**_input)
+            if len(output) == 0:
+                continue
+            compressed_docs.append(Document(page_content=output, metadata=doc.metadata))
+        return compressed_docs
+
+    async def acompress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        raise NotImplementedError
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLanguageModel,
+        prompt: Optional[PromptTemplate] = None,
+        get_input: Optional[Callable[[str, Document], str]] = None,
+    ) -> "LLMChainExtractor":
+        """Initialize from LLM."""
+        _prompt = prompt if prompt is not None else _get_default_chain_prompt()
+        _get_input = get_input if get_input is not None else default_get_input
+        llm_chain = LLMChain(llm=llm, prompt=_prompt)
+        return cls(llm_chain=llm_chain, get_input=_get_input)

langchain/retrievers/document_compressors/chain_extract_prompt.py

@@ -0,0 +1,11 @@
+# flake8: noqa
+prompt_template = """Given the following question and context, extract any part of the context *AS IS* that is relevant to answer the question. If none of the context is relevant return {no_output_str}. 
+
+Remember, *DO NOT* edit the extracted parts of the context.
+
+> Question: {{question}}
+> Context:
+>>>
+{{context}}
+>>>
+Extracted relevant parts:"""

langchain/retrievers/document_compressors/chain_filter.py

@@ -0,0 +1,65 @@
+"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
+from typing import Any, Callable, Dict, Optional, Sequence
+
+from langchain import BasePromptTemplate, LLMChain, PromptTemplate
+from langchain.output_parsers.boolean import BooleanOutputParser
+from langchain.retrievers.document_compressors.base import (
+    BaseDocumentCompressor,
+)
+from langchain.retrievers.document_compressors.chain_filter_prompt import (
+    prompt_template,
+)
+from langchain.schema import BaseLanguageModel, Document
+
+
+def _get_default_chain_prompt() -> PromptTemplate:
+    return PromptTemplate(
+        template=prompt_template,
+        input_variables=["question", "context"],
+        output_parser=BooleanOutputParser(),
+    )
+
+
+def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
+    """Return the compression chain input."""
+    return {"question": query, "context": doc.page_content}
+
+
+class LLMChainFilter(BaseDocumentCompressor):
+    """Filter that drops documents that aren't relevant to the query."""
+
+    llm_chain: LLMChain
+    """LLM wrapper to use for filtering documents. 
+    The chain prompt is expected to have a BooleanOutputParser."""
+
+    get_input: Callable[[str, Document], dict] = default_get_input
+    """Callable for constructing the chain input from the query and a Document."""
+
+    def compress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Filter down documents based on their relevance to the query."""
+        filtered_docs = []
+        for doc in documents:
+            _input = self.get_input(query, doc)
+            include_doc = self.llm_chain.predict_and_parse(**_input)
+            if include_doc:
+                filtered_docs.append(doc)
+        return filtered_docs
+
+    async def acompress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Filter down documents."""
+        raise NotImplementedError
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLanguageModel,
+        prompt: Optional[BasePromptTemplate] = None,
+        **kwargs: Any
+    ) -> "LLMChainFilter":
+        _prompt = prompt if prompt is not None else _get_default_chain_prompt()
+        llm_chain = LLMChain(llm=llm, prompt=_prompt)
+        return cls(llm_chain=llm_chain, **kwargs)

langchain/retrievers/document_compressors/chain_filter_prompt.py

@@ -0,0 +1,9 @@
+# flake8: noqa
+prompt_template = """Given the following question and context, return YES if the context is relevant to the question and NO if it isn't.
+
+> Question: {question}
+> Context:
+>>>
+{context}
+>>>
+> Relevant (YES / NO):"""

langchain/retrievers/document_compressors/embeddings_filter.py

@@ -0,0 +1,70 @@
+"""Document compressor that uses embeddings to drop documents unrelated to the query."""
+from typing import Callable, Dict, Optional, Sequence
+
+import numpy as np
+from pydantic import root_validator
+
+from langchain.document_transformers import (
+    _get_embeddings_from_stateful_docs,
+    get_stateful_documents,
+)
+from langchain.embeddings.base import Embeddings
+from langchain.math_utils import cosine_similarity
+from langchain.retrievers.document_compressors.base import (
+    BaseDocumentCompressor,
+)
+from langchain.schema import Document
+
+
+class EmbeddingsFilter(BaseDocumentCompressor):
+    embeddings: Embeddings
+    """Embeddings to use for embedding document contents and queries."""
+    similarity_fn: Callable = cosine_similarity
+    """Similarity function for comparing documents. Function expected to take as input
+    two matrices (List[List[float]]) and return a matrix of scores where higher values
+    indicate greater similarity."""
+    k: Optional[int] = 20
+    """The number of relevant documents to return. Can be set to None, in which case
+    `similarity_threshold` must be specified. Defaults to 20."""
+    similarity_threshold: Optional[float]
+    """Threshold for determining when two documents are similar enough
+    to be considered redundant. Defaults to None, must be specified if `k` is set
+    to None."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    @root_validator()
+    def validate_params(cls, values: Dict) -> Dict:
+        """Validate similarity parameters."""
+        if values["k"] is None and values["similarity_threshold"] is None:
+            raise ValueError("Must specify one of `k` or `similarity_threshold`.")
+        return values
+
+    def compress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Filter documents based on similarity of their embeddings to the query."""
+        stateful_documents = get_stateful_documents(documents)
+        embedded_documents = _get_embeddings_from_stateful_docs(
+            self.embeddings, stateful_documents
+        )
+        embedded_query = self.embeddings.embed_query(query)
+        similarity = self.similarity_fn([embedded_query], embedded_documents)[0]
+        included_idxs = np.arange(len(embedded_documents))
+        if self.k is not None:
+            included_idxs = np.argsort(similarity)[::-1][: self.k]
+        if self.similarity_threshold is not None:
+            similar_enough = np.where(
+                similarity[included_idxs] > self.similarity_threshold
+            )
+            included_idxs = included_idxs[similar_enough]
+        return [stateful_documents[i] for i in included_idxs]
+
+    async def acompress_documents(
+        self, documents: Sequence[Document], query: str
+    ) -> Sequence[Document]:
+        """Filter down documents."""
+        raise NotImplementedError

langchain/schema.py

@@ -2,7 +2,17 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Generic, List, NamedTuple, Optional, TypeVar, Union
+from typing import (
+    Any,
+    Dict,
+    Generic,
+    List,
+    NamedTuple,
+    Optional,
+    Sequence,
+    TypeVar,
+    Union,
+)
 
 from pydantic import BaseModel, Extra, Field, root_validator
 
@@ -394,16 +404,17 @@ class OutputParserException(Exception):
     pass
 
 
-D = TypeVar("D", bound=Document)
-
-
-class BaseDocumentTransformer(ABC, Generic[D]):
+class BaseDocumentTransformer(ABC):
     """Base interface for transforming documents."""
 
     @abstractmethod
-    def transform_documents(self, documents: List[D], **kwargs: Any) -> List[D]:
+    def transform_documents(
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
         """Transform a list of documents."""
 
     @abstractmethod
-    async def atransform_documents(self, documents: List[D], **kwargs: Any) -> List[D]:
+    async def atransform_documents(
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
         """Asynchronously transform a list of documents."""

langchain/text_splitter.py

@@ -13,6 +13,7 @@ from typing import (
     List,
     Literal,
     Optional,
+    Sequence,
     Union,
 )
 
@@ -22,7 +23,7 @@ from langchain.schema import BaseDocumentTransformer
 logger = logging.getLogger(__name__)
 
 
-class TextSplitter(BaseDocumentTransformer[Document], ABC):
+class TextSplitter(BaseDocumentTransformer, ABC):
     """Interface for splitting text into chunks."""
 
     def __init__(
@@ -63,7 +64,7 @@ class TextSplitter(BaseDocumentTransformer[Document], ABC):
         """Split documents."""
         texts = [doc.page_content for doc in documents]
         metadatas = [doc.metadata for doc in documents]
-        return self.create_documents(texts, metadatas)
+        return self.create_documents(texts, metadatas=metadatas)
 
     def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
         text = separator.join(docs)
@@ -173,15 +174,15 @@ class TextSplitter(BaseDocumentTransformer[Document], ABC):
         return cls(length_function=_tiktoken_encoder, **kwargs)
 
     def transform_documents(
-        self, documents: List[Document], **kwargs: Any
-    ) -> List[Document]:
-        """Transform list of documents by splitting them."""
-        return self.split_documents(documents)
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
+        """Transform sequence of documents by splitting them."""
+        return self.split_documents(list(documents))
 
     async def atransform_documents(
-        self, documents: List[Document], **kwargs: Any
-    ) -> List[Document]:
-        """Asynchronously transform a list of documents by splitting them."""
+        self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
+        """Asynchronously transform a sequence of documents by splitting them."""
         raise NotImplementedError
 
 

langchain/tools/sql_database/tool.py

@@ -6,7 +6,7 @@ from typing import Any, Dict
 from langchain.chains.llm import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain.sql_database import SQLDatabase
-from langchain.llms.base import BaseLLM
+from langchain.schema import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.tools.sql_database.prompt import QUERY_CHECKER
 
@@ -81,7 +81,7 @@ class QueryCheckerTool(BaseSQLDatabaseTool, BaseTool):
     Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
 
     template: str = QUERY_CHECKER
-    llm: BaseLLM
+    llm: BaseLanguageModel
     llm_chain: LLMChain = Field(init=False)
     name = "query_checker_sql_db"
     description = """

langchain/utilities/jira.py

@@ -159,7 +159,7 @@ class JiraAPIWrapper(BaseModel):
                 "json is not installed. " "Please install it with `pip install json`"
             )
         params = json.loads(query)
-        return self.jira.create_issue(fields=dict(params))
+        return self.jira.issue_create(fields=dict(params))
 
     def other(self, query: str) -> str:
         context = {"self": self}

langchain/vectorstores/opensearch_vector_search.py

@@ -168,6 +168,21 @@ def _approximate_search_query_with_boolean_filter(
     }
 
 
+def _approximate_search_query_with_lucene_filter(
+    query_vector: List[float],
+    lucene_filter: Dict,
+    size: int = 4,
+    k: int = 4,
+    vector_field: str = "vector_field",
+) -> Dict:
+    """For Approximate k-NN Search, with Lucene Filter."""
+    search_query = _default_approximate_search_query(
+        query_vector, size, k, vector_field
+    )
+    search_query["query"]["knn"][vector_field]["filter"] = lucene_filter
+    return search_query
+
+
 def _default_script_query(
     query_vector: List[float],
     space_type: str = "l2",
@@ -340,10 +355,14 @@ class OpenSearchVectorSearch(VectorStore):
             size: number of results the query actually returns; default: 4
 
             boolean_filter: A Boolean filter consists of a Boolean query that
-            contains a k-NN query and a filter
+            contains a k-NN query and a filter.
 
             subquery_clause: Query clause on the knn vector field; default: "must"
 
+            lucene_filter: the Lucene algorithm decides whether to perform an exact
+            k-NN search with pre-filtering or an approximate search with modified
+            post-filtering.
+
         Optional Args for Script Scoring Search:
             search_type: "script_scoring"; default: "approximate_search"
 
@@ -371,10 +390,20 @@ class OpenSearchVectorSearch(VectorStore):
             size = _get_kwargs_value(kwargs, "size", 4)
             boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
             subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
+            lucene_filter = _get_kwargs_value(kwargs, "lucene_filter", {})
+            if boolean_filter != {} and lucene_filter != {}:
+                raise ValueError(
+                    "Both `boolean_filter` and `lucene_filter` are provided which "
+                    "is invalid"
+                )
             if boolean_filter != {}:
                 search_query = _approximate_search_query_with_boolean_filter(
                     embedding, boolean_filter, size, k, vector_field, subquery_clause
                 )
+            elif lucene_filter != {}:
+                search_query = _approximate_search_query_with_lucene_filter(
+                    embedding, lucene_filter, size, k, vector_field
+                )
             else:
                 search_query = _default_approximate_search_query(
                     embedding, size, k, vector_field
@@ -442,7 +471,7 @@ class OpenSearchVectorSearch(VectorStore):
             to "text".
 
         Optional Keyword Args for Approximate Search:
-            engine: "nmslib", "faiss", "hnsw"; default: "nmslib"
+            engine: "nmslib", "faiss", "lucene"; default: "nmslib"
 
             space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
 

langchain/vectorstores/utils.py

@@ -4,10 +4,7 @@ from typing import List
 
 import numpy as np
 
-
-def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
-    """Calculate cosine similarity with numpy."""
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+from langchain.math_utils import cosine_similarity
 
 
 def maximal_marginal_relevance(
@@ -17,22 +14,26 @@ def maximal_marginal_relevance(
     k: int = 4,
 ) -> List[int]:
     """Calculate maximal marginal relevance."""
-    idxs: List[int] = []
-    while len(idxs) < k:
+    if min(k, len(embedding_list)) <= 0:
+        return []
+    similarity_to_query = cosine_similarity([query_embedding], embedding_list)[0]
+    most_similar = int(np.argmax(similarity_to_query))
+    idxs = [most_similar]
+    selected = np.array([embedding_list[most_similar]])
+    while len(idxs) < min(k, len(embedding_list)):
         best_score = -np.inf
         idx_to_add = -1
-        for i, emb in enumerate(embedding_list):
+        similarity_to_selected = cosine_similarity(embedding_list, selected)
+        for i, query_score in enumerate(similarity_to_query):
             if i in idxs:
                 continue
-            first_part = cosine_similarity(query_embedding, emb)
-            second_part = 0.0
-            for j in idxs:
-                cos_sim = cosine_similarity(emb, embedding_list[j])
-                if cos_sim > second_part:
-                    second_part = cos_sim
-            equation_score = lambda_mult * first_part - (1 - lambda_mult) * second_part
+            redundant_score = max(similarity_to_selected[i])
+            equation_score = (
+                lambda_mult * query_score - (1 - lambda_mult) * redundant_score
+            )
             if equation_score > best_score:
                 best_score = equation_score
                 idx_to_add = i
         idxs.append(idx_to_add)
+        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
     return idxs

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.145"
+version = "0.0.146"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -148,6 +148,9 @@ select = [
   "F",  # pyflakes
   "I",  # isort
 ]
+exclude = [
+  "tests/integration_tests/examples/non-utf8-encoding.py",
+]
 
 [tool.mypy]
 ignore_missing_imports = "True"

tests/integration_tests/.env.example

@@ -6,4 +6,12 @@ OPENAI_API_KEY=
 # your api key from left menu "API Keys" in https://app.pinecone.io
 PINECONE_API_KEY=your_pinecone_api_key_here
 # your pinecone environment from left menu "API Keys" in https://app.pinecone.io
-PINECONE_ENVIRONMENT=us-west4-gcp
+PINECONE_ENVIRONMENT=us-west4-gcp
+
+
+# jira
+# your api token from https://id.atlassian.com/manage-profile/security/api-tokens
+# more details here: https://confluence.atlassian.com/enterprise/using-personal-access-tokens-1026032365.html
+# JIRA_API_TOKEN=your_jira_api_token_here
+# JIRA_USERNAME=your_jira_username_here
+# JIRA_INSTANCE_URL=your_jira_instance_url_here

tests/integration_tests/document_loaders/test_python.py

@@ -0,0 +1,19 @@
+from pathlib import Path
+
+import pytest
+
+from langchain.document_loaders.python import PythonLoader
+
+
+@pytest.mark.parametrize("filename", ["default-encoding.py", "non-utf8-encoding.py"])
+def test_python_loader(filename: str) -> None:
+    """Test Python loader."""
+    file_path = Path(__file__).parent.parent / "examples" / filename
+    loader = PythonLoader(str(file_path))
+    docs = loader.load()
+
+    assert len(docs) == 1
+
+    metadata = docs[0].metadata
+
+    assert metadata["source"] == str(file_path)

tests/integration_tests/examples/default-encoding.py

@@ -0,0 +1 @@
+u = "🦜🔗"

tests/integration_tests/examples/non-utf8-encoding.py

@@ -0,0 +1,3 @@
+# coding: iso-8859-5
+# <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <- Cyrillic characters
+u = "<EFBFBD><EFBFBD><EFBFBD><EFBFBD>"

tests/integration_tests/retrievers/document_compressors/test_base.py

@@ -0,0 +1,28 @@
+"""Integration test for compression pipelines."""
+from langchain.document_transformers import EmbeddingsRedundantFilter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.retrievers.document_compressors import (
+    DocumentCompressorPipeline,
+    EmbeddingsFilter,
+)
+from langchain.schema import Document
+from langchain.text_splitter import CharacterTextSplitter
+
+
+def test_document_compressor_pipeline() -> None:
+    embeddings = OpenAIEmbeddings()
+    splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator=". ")
+    redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
+    relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.8)
+    pipeline_filter = DocumentCompressorPipeline(
+        transformers=[splitter, redundant_filter, relevant_filter]
+    )
+    texts = [
+        "This sentence is about cows",
+        "This sentence was about cows",
+        "foo bar baz",
+    ]
+    docs = [Document(page_content=". ".join(texts))]
+    actual = pipeline_filter.compress_documents(docs, "Tell me about farm animals")
+    assert len(actual) == 1
+    assert actual[0].page_content in texts[:2]

tests/integration_tests/retrievers/document_compressors/test_chain_extract.py

@@ -0,0 +1,36 @@
+"""Integration test for LLMChainExtractor."""
+from langchain.chat_models import ChatOpenAI
+from langchain.retrievers.document_compressors import LLMChainExtractor
+from langchain.schema import Document
+
+
+def test_llm_chain_extractor() -> None:
+    texts = [
+        "The Roman Empire followed the Roman Republic.",
+        "I love chocolate chip cookies—my mother makes great cookies.",
+        "The first Roman emperor was Caesar Augustus.",
+        "Don't you just love Caesar salad?",
+        "The Roman Empire collapsed in 476 AD after the fall of Rome.",
+        "Let's go to Olive Garden!",
+    ]
+    doc = Document(page_content=" ".join(texts))
+    compressor = LLMChainExtractor.from_llm(ChatOpenAI())
+    actual = compressor.compress_documents([doc], "Tell me about the Roman Empire")[
+        0
+    ].page_content
+    expected_returned = [0, 2, 4]
+    expected_not_returned = [1, 3, 5]
+    assert all([texts[i] in actual for i in expected_returned])
+    assert all([texts[i] not in actual for i in expected_not_returned])
+
+
+def test_llm_chain_extractor_empty() -> None:
+    texts = [
+        "I love chocolate chip cookies—my mother makes great cookies.",
+        "Don't you just love Caesar salad?",
+        "Let's go to Olive Garden!",
+    ]
+    doc = Document(page_content=" ".join(texts))
+    compressor = LLMChainExtractor.from_llm(ChatOpenAI())
+    actual = compressor.compress_documents([doc], "Tell me about the Roman Empire")
+    assert len(actual) == 0

tests/integration_tests/retrievers/document_compressors/test_chain_filter.py

@@ -0,0 +1,17 @@
+"""Integration test for llm-based relevant doc filtering."""
+from langchain.chat_models import ChatOpenAI
+from langchain.retrievers.document_compressors import LLMChainFilter
+from langchain.schema import Document
+
+
+def test_llm_chain_filter() -> None:
+    texts = [
+        "What happened to all of my cookies?",
+        "I wish there were better Italian restaurants in my neighborhood.",
+        "My favorite color is green",
+    ]
+    docs = [Document(page_content=t) for t in texts]
+    relevant_filter = LLMChainFilter.from_llm(llm=ChatOpenAI())
+    actual = relevant_filter.compress_documents(docs, "Things I said related to food")
+    assert len(actual) == 2
+    assert len(set(texts[:2]).intersection([d.page_content for d in actual])) == 2

tests/integration_tests/retrievers/document_compressors/test_embeddings_filter.py

@@ -0,0 +1,39 @@
+"""Integration test for embedding-based relevant doc filtering."""
+import numpy as np
+
+from langchain.document_transformers import _DocumentWithState
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.retrievers.document_compressors import EmbeddingsFilter
+from langchain.schema import Document
+
+
+def test_embeddings_filter() -> None:
+    texts = [
+        "What happened to all of my cookies?",
+        "I wish there were better Italian restaurants in my neighborhood.",
+        "My favorite color is green",
+    ]
+    docs = [Document(page_content=t) for t in texts]
+    embeddings = OpenAIEmbeddings()
+    relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
+    actual = relevant_filter.compress_documents(docs, "What did I say about food?")
+    assert len(actual) == 2
+    assert len(set(texts[:2]).intersection([d.page_content for d in actual])) == 2
+
+
+def test_embeddings_filter_with_state() -> None:
+    texts = [
+        "What happened to all of my cookies?",
+        "I wish there were better Italian restaurants in my neighborhood.",
+        "My favorite color is green",
+    ]
+    query = "What did I say about food?"
+    embeddings = OpenAIEmbeddings()
+    embedded_query = embeddings.embed_query(query)
+    state = {"embedded_doc": np.zeros(len(embedded_query))}
+    docs = [_DocumentWithState(page_content=t, state=state) for t in texts]
+    docs[-1].state = {"embedded_doc": embedded_query}
+    relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
+    actual = relevant_filter.compress_documents(docs, query)
+    assert len(actual) == 1
+    assert texts[-1] == actual[0].page_content

tests/integration_tests/retrievers/test_contextual_compression.py

@@ -0,0 +1,25 @@
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain.retrievers.document_compressors import EmbeddingsFilter
+from langchain.vectorstores import Chroma
+
+
+def test_contextual_compression_retriever_get_relevant_docs() -> None:
+    """Test get_relevant_docs."""
+    texts = [
+        "This is a document about the Boston Celtics",
+        "The Boston Celtics won the game by 20 points",
+        "I simply love going to the movies",
+    ]
+    embeddings = OpenAIEmbeddings()
+    base_compressor = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
+    base_retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
+        search_kwargs={"k": len(texts)}
+    )
+    retriever = ContextualCompressionRetriever(
+        base_compressor=base_compressor, base_retriever=base_retriever
+    )
+
+    actual = retriever.get_relevant_documents("Tell me about the Celtics")
+    assert len(actual) == 2
+    assert texts[-1] not in [d.page_content for d in actual]

tests/integration_tests/test_document_transformers.py

@@ -0,0 +1,31 @@
+"""Integration test for embedding-based redundant doc filtering."""
+from langchain.document_transformers import (
+    EmbeddingsRedundantFilter,
+    _DocumentWithState,
+)
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.schema import Document
+
+
+def test_embeddings_redundant_filter() -> None:
+    texts = [
+        "What happened to all of my cookies?",
+        "Where did all of my cookies go?",
+        "I wish there were better Italian restaurants in my neighborhood.",
+    ]
+    docs = [Document(page_content=t) for t in texts]
+    embeddings = OpenAIEmbeddings()
+    redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
+    actual = redundant_filter.transform_documents(docs)
+    assert len(actual) == 2
+    assert set(texts[:2]).intersection([d.page_content for d in actual])
+
+
+def test_embeddings_redundant_filter_with_state() -> None:
+    texts = ["What happened to all of my cookies?", "foo bar baz"]
+    state = {"embedded_doc": [0.5] * 10}
+    docs = [_DocumentWithState(page_content=t, state=state) for t in texts]
+    embeddings = OpenAIEmbeddings()
+    redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
+    actual = redundant_filter.transform_documents(docs)
+    assert len(actual) == 1

tests/integration_tests/utilities/test_jira_api.py

@@ -0,0 +1,27 @@
+"""Integration test for JIRA API Wrapper."""
+from langchain.utilities.jira import JiraAPIWrapper
+import json
+
+
+def test_search() -> None:
+    """Test for Searching issues on JIRA"""
+    jql = "project = TP"
+    jira = JiraAPIWrapper()
+    output = jira.run("jql", jql)
+    assert "issues" in output
+
+
+def test_getprojects() -> None:
+    """Test for getting projects on JIRA"""
+    jira = JiraAPIWrapper()
+    output = jira.run("get_projects", "")
+    assert "projects" in output
+
+
+def test_create_ticket() -> None:
+    """Test the Create Ticket Call that Creates a Issue/Ticket on JIRA."""
+    issue_string = '{"summary": "Test Summary", "description": "Test Description", "issuetype": {"name": "Bug"}, "project": {"key": "TP"}}'
+    jira = JiraAPIWrapper()
+    output = jira.run("create_issue", issue_string)
+    assert "id" in output
+    assert "key" in output

tests/integration_tests/vectorstores/test_opensearch.py

@@ -164,3 +164,13 @@ def test_appx_search_with_boolean_filter() -> None:
         "foo", k=3, boolean_filter=boolean_filter_val, subquery_clause="should"
     )
     assert output == [Document(page_content="bar")]
+
+
+def test_appx_search_with_lucene_filter() -> None:
+    """Test Approximate Search with Lucene Filter."""
+    lucene_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="lucene"
+    )
+    output = docsearch.similarity_search("foo", k=3, lucene_filter=lucene_filter_val)
+    assert output == [Document(page_content="bar")]

tests/unit_tests/test_document_transformers.py

@@ -0,0 +1,15 @@
+"""Unit tests for document transformers."""
+from langchain.document_transformers import _filter_similar_embeddings
+from langchain.math_utils import cosine_similarity
+
+
+def test__filter_similar_embeddings() -> None:
+    threshold = 0.79
+    embedded_docs = [[1.0, 2.0], [1.0, 2.0], [2.0, 1.0], [2.0, 0.5], [0.0, 0.0]]
+    expected = [1, 3, 4]
+    actual = _filter_similar_embeddings(embedded_docs, cosine_similarity, threshold)
+    assert expected == actual
+
+
+def test__filter_similar_embeddings_empty() -> None:
+    assert len(_filter_similar_embeddings([], cosine_similarity, 0.0)) == 0

tests/unit_tests/test_math_utils.py

@@ -0,0 +1,39 @@
+"""Test math utility functions."""
+from typing import List
+
+import numpy as np
+
+from langchain.math_utils import cosine_similarity
+
+
+def test_cosine_similarity_zero() -> None:
+    X = np.zeros((3, 3))
+    Y = np.random.random((3, 3))
+    expected = np.zeros((3, 3))
+    actual = cosine_similarity(X, Y)
+    assert np.allclose(expected, actual)
+
+
+def test_cosine_similarity_identity() -> None:
+    X = np.random.random((4, 4))
+    expected = np.ones(4)
+    actual = np.diag(cosine_similarity(X, X))
+    assert np.allclose(expected, actual)
+
+
+def test_cosine_similarity_empty() -> None:
+    empty_list: List[List[float]] = []
+    assert len(cosine_similarity(empty_list, empty_list)) == 0
+    assert len(cosine_similarity(empty_list, np.random.random((3, 3)))) == 0
+
+
+def test_cosine_similarity() -> None:
+    X = [[1.0, 2.0, 3.0], [0.0, 1.0, 0.0], [1.0, 2.0, 0.0]]
+    Y = [[0.5, 1.0, 1.5], [1.0, 0.0, 0.0], [2.0, 5.0, 2.0]]
+    expected = [
+        [1.0, 0.26726124, 0.83743579],
+        [0.53452248, 0.0, 0.87038828],
+        [0.5976143, 0.4472136, 0.93419873],
+    ]
+    actual = cosine_similarity(X, Y)
+    assert np.allclose(expected, actual)