Added whisper compatibility (#1421)

TODO:
- fix compatibility with base

Co-authored-by: Klein Tahiraj
<62718109+klein-t@users.noreply.github.com>

---------

Co-authored-by: Klein Tahiraj <62718109+klein-t@users.noreply.github.com>
Co-authored-by: Klein Tahiraj <klein.tahiraj@gmail.com>

.gitignore

@@ -106,6 +106,7 @@ celerybeat.pid
 
 # Environments
 .env
+.envrc
 .venv
 .venvs
 env/

Makefile

@@ -21,13 +21,12 @@ docs_linkcheck:
 
 format:
 	poetry run black .
-	poetry run isort .
+	poetry run ruff --select I --fix .
 
 lint:
 	poetry run mypy .
 	poetry run black . --check
-	poetry run isort . --check
-	poetry run flake8 .
+	poetry run ruff .
 
 test:
 	poetry run pytest tests/unit_tests

README.md

@@ -42,7 +42,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
 - Getting started (installation, setting up the environment, simple examples)
 - How-To examples (demos, integrations, helper functions)
 - Reference (full API docs)
-  Resources (high-level explanation of core concepts)
+- Resources (high-level explanation of core concepts)
 
 ## 🚀 What can this help with?
 

docs/ecosystem/atlas.md

@@ -0,0 +1,25 @@
+# AtlasDB
+
+This page covers how to Nomic's Atlas ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
+
+## Installation and Setup
+- Install the Python package with `pip install nomic`
+- Nomic is also included in langchains poetry extras `poetry install -E all`
+- 
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
+This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
+Please see [the Nomic docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
+
+
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import AtlasDB
+```
+
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/bananadev.md

@@ -4,24 +4,28 @@ This page covers how to use the Banana ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
 
 ## Installation and Setup
+
 - Install with `pip3 install banana-dev`
-- Get an CerebriumAI api key and set it as an environment variable (`BANANA_API_KEY`)
+- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
 
 ## Define your Banana Template
 
-If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base). 
-This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/). 
+If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
+This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
 You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
 
 ## Build the Banana app
 
-You must include a output in the result. There is a rigid response structure.
+Banana Apps must include the "output" key in the return json. 
+There is a rigid response structure.
+
 ```python
 # Return the results as a dictionary
 result = {'output': result}
 ```
 
 An example inference function would be:
+
 ```python
 def inference(model_inputs:dict) -> dict:
     global model
@@ -31,22 +35,22 @@ def inference(model_inputs:dict) -> dict:
     prompt = model_inputs.get('prompt', None)
     if prompt == None:
         return {'message': "No prompt provided"}
-    
+
     # Run the model
     input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
     output = model.generate(
-        input_ids, 
-        max_length=100, 
-        do_sample=True, 
-        top_k=50, 
-        top_p=0.95, 
-        num_return_sequences=1, 
-        temperature=0.9, 
-        early_stopping=True, 
-        no_repeat_ngram_size=3, 
-        num_beams=5, 
-        length_penalty=1.5, 
-        repetition_penalty=1.5, 
+        input_ids,
+        max_length=100,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+        temperature=0.9,
+        early_stopping=True,
+        no_repeat_ngram_size=3,
+        num_beams=5,
+        length_penalty=1.5,
+        repetition_penalty=1.5,
         bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
         )
 
@@ -58,17 +62,18 @@ def inference(model_inputs:dict) -> dict:
 
 You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
 
-
 ## Wrappers
 
 ### LLM
 
-There exists an Banana LLM wrapper, which you can access with 
+There exists an Banana LLM wrapper, which you can access with
+
 ```python
 from langchain.llms import Banana
 ```
 
 You need to provide a model key located in the dashboard:
+
 ```python
 llm = Banana(model_key="YOUR_MODEL_KEY")
-```
+```

docs/ecosystem/deeplake.md

@@ -0,0 +1,25 @@
+# Deep Lake
+
+This page covers how to use the Deep Lake ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Deep Lake wrappers. For more information.
+
+1. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
+
+2. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Getting Started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
+
+## Installation and Setup
+- Install the Python package with `pip install deeplake`
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vectorstore (for now), whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import DeepLake
+```
+
+
+For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstore_examples/deeplake.ipynb)

docs/getting_started/getting_started.md

@@ -66,7 +66,7 @@ llm = OpenAI(temperature=0.9)
 We can now call it on some input!
 
 ```python
-text = "What would be a good company name a company that makes colorful socks?"
+text = "What would be a good company name for a company that makes colorful socks?"
 print(llm(text))
 ```
 

docs/modules/agents.rst

@@ -2,7 +2,7 @@ Agents
 ==========================
 
 Some applications will require not just a predetermined chain of calls to LLMs/other tools,
-but potentially an unknown chain that depends on the user input.
+but potentially an unknown chain that depends on the user's input.
 In these types of chains, there is a “agent” which has access to a suite of tools.
 Depending on the user input, the agent can then decide which, if any, of these tools to call.
 
@@ -12,7 +12,7 @@ The following sections of documentation are provided:
 
 - `Key Concepts <./agents/key_concepts.html>`_: A conceptual guide going over the various concepts related to agents.
 
-- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agent, and how to customize agents.
+- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agents, and how to customize agents.
 
 - `Reference <../reference/modules/agents.html>`_: API reference documentation for all Agent classes.
 
@@ -27,4 +27,4 @@ The following sections of documentation are provided:
    ./agents/getting_started.ipynb
    ./agents/key_concepts.md
    ./agents/how_to_guides.rst
-   Reference<../reference/modules/agents.rst>
+   Reference<../reference/modules/agents.rst>

docs/modules/agents/agent_toolkits/csv.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7094e328",
+   "metadata": {},
+   "source": [
+    "# CSV Agent\n",
+    "\n",
+    "This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.\n",
+    "\n",
+    "**NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "827982c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_csv_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "caae0bec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16c4dc59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "46b9489d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 891 rows in the dataframe.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a96309be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'30 people have more than 3 siblings.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many people have more than 3 sibligngs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "964a09f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 5.449689683556195\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5.449689683556195'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats the square root of the average age?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551de2be",
+   "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/agents/agent_toolkits/json.ipynb

@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
+   "metadata": {},
+   "source": [
+    "# JSON Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The agent is able to iteratively explore the blob to find what it needs to answer the user's question.\n",
+    "\n",
+    "In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml).\n",
+    "\n",
+    "We will use the JSON agent to answer some questions about the API spec."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
+   "metadata": {},
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "from langchain.agents import (\n",
+    "    create_json_agent,\n",
+    "    AgentExecutor\n",
+    ")\n",
+    "from langchain.agents.agent_toolkits import JsonToolkit\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.requests import RequestsWrapper\n",
+    "from langchain.tools.json.tool import JsonSpec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"openai_openapi.yml\") as f:\n",
+    "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
+    "json_toolkit = JsonToolkit(spec=json_spec)\n",
+    "\n",
+    "json_agent_executor = create_json_agent(\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=json_toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05cfcb24-4389-4b8f-ad9e-466e3fca8db0",
+   "metadata": {},
+   "source": [
+    "## Example: getting the required POST parameters for a request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "faf13702-50f0-4d1b-b91f-48c750ccfd98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mTrue\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The required parameters in the request body to the /completions endpoint are 'model'.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The required parameters in the request body to the /completions endpoint are 'model'.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_agent_executor.run(\"What are the required parameters in the request body to the /completions endpoint?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba9c9d30",
+   "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/modules/agents/agent_toolkits/openapi.ipynb

@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
+   "metadata": {},
+   "source": [
+    "# OpenAPI Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with an OpenAPI spec and make a correct API request based on the information it has gathered from the spec.\n",
+    "\n",
+    "In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
+   "metadata": {},
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "from langchain.agents import create_openapi_agent\n",
+    "from langchain.agents.agent_toolkits import OpenAPIToolkit\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.requests import RequestsWrapper\n",
+    "from langchain.tools.json.tool import JsonSpec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"openai_openapi.yml\") as f:\n",
+    "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "json_spec=JsonSpec(dict_=data, max_value_length=4000)\n",
+    "headers = {\n",
+    "    \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"\n",
+    "}\n",
+    "requests_wrapper=RequestsWrapper(headers=headers)\n",
+    "openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, requests_wrapper, verbose=True)\n",
+    "openapi_agent_executor = create_openapi_agent(\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=openapi_toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f111879d-ae84-41f9-ad82-d3e6b72c41ba",
+   "metadata": {},
+   "source": [
+    "## Example: agent capable of analyzing OpenAPI spec and making requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "548db7f7-337b-4ba8-905c-e7fd58c01799",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_explorer\n",
+      "Action Input: What is the base url for the API?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the servers key to see what the base url is\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"servers\"][0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"servers\"][0]` is not a dict, get the value directly.')\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should get the value of the servers key\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"servers\"][0]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{'url': 'https://api.openai.com/v1'}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the base url for the API\n",
+      "Final Answer: The base url for the API is https://api.openai.com/v1\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mThe base url for the API is https://api.openai.com/v1\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should find the path for the /completions endpoint.\n",
+      "Action: json_explorer\n",
+      "Action Input: What is the path for the /completions endpoint?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the path for the /completions endpoint\n",
+      "Final Answer: data[\"paths\"][2]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mdata[\"paths\"][2]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should find the required parameters for the POST request.\n",
+      "Action: json_explorer\n",
+      "Action Input: What are the required parameters for a POST request to the /completions endpoint?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]` is not a dict, get the value directly.')\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to get the value directly\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mThe required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the parameters needed to make the request.\n",
+      "Action: requests_post\n",
+      "Action Input: { \"url\": \"https://api.openai.com/v1/completions\", \"data\": { \"model\": \"davinci\", \"prompt\": \"tell me a joke\" } }\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{\"id\":\"cmpl-6oeEcNETfq8TOuIUQvAct6NrBXihs\",\"object\":\"text_completion\",\"created\":1677529082,\"model\":\"davinci\",\"choices\":[{\"text\":\"\\n\\n\\n\\nLove is a battlefield\\n\\n\\n\\nIt's me...And some\",\"index\":0,\"logprobs\":null,\"finish_reason\":\"length\"}],\"usage\":{\"prompt_tokens\":4,\"completion_tokens\":16,\"total_tokens\":20}}\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Love is a battlefield. It's me...And some.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Love is a battlefield. It's me...And some.\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openapi_agent_executor.run(\"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ec9582b",
+   "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/modules/agents/agent_toolkits/pandas.ipynb

@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c81da886",
+   "metadata": {},
+   "source": [
+    "# Pandas Dataframe Agent\n",
+    "\n",
+    "This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.\n",
+    "\n",
+    "**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0cdd9bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_pandas_dataframe_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "051ebe84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('titanic.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4185ff46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a9207a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 891 rows in the dataframe.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bd43617c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'30 people have more than 3 siblings.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many people have more than 3 sibligngs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "94e64b58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 5.449689683556195\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5.449689683556195'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats the square root of the average age?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eba13b4d",
+   "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/agents/agent_toolkits/python.ipynb

@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82a4c2cc-20ea-4b20-a565-63e905dee8ff",
+   "metadata": {},
+   "source": [
+    "## Python Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to write and execute python code to answer a question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import create_python_agent\n",
+    "from langchain.tools.python.tool import PythonREPLTool\n",
+    "from langchain.python import PythonREPL\n",
+    "from langchain.llms.openai import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cc422f53-c51c-4694-a834-72ecd1e68363",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor = create_python_agent(\n",
+    "    llm=OpenAI(temperature=0, max_tokens=1000),\n",
+    "    tool=PythonREPLTool(),\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c16161de",
+   "metadata": {},
+   "source": [
+    "## Fibonacci Example\n",
+    "This example was created by [John Wiseman](https://twitter.com/lemonodor/status/1628270074074398720?s=20)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "25cd4f92-ea9b-4fe6-9838-a4f85f81eebe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to calculate the 10th fibonacci number\n",
+      "Action: Python REPL\n",
+      "Action Input: def fibonacci(n):\n",
+      "    if n == 0:\n",
+      "        return 0\n",
+      "    elif n == 1:\n",
+      "        return 1\n",
+      "    else:\n",
+      "        return fibonacci(n-1) + fibonacci(n-2)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to call the function with 10 as the argument\n",
+      "Action: Python REPL\n",
+      "Action Input: fibonacci(10)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 55\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'55'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What is the 10th fibonacci number?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caa30de",
+   "metadata": {},
+   "source": [
+    "## Training neural net\n",
+    "This example was created by [Samee Ur Rehman](https://twitter.com/sameeurehman/status/1630130518133207046?s=20)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4b9f60e7-eb6a-4f14-8604-498d863d4482",
+   "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 I need to write a neural network in PyTorch and train it on the given data.\n",
+      "Action: Python REPL\n",
+      "Action Input: \n",
+      "import torch\n",
+      "\n",
+      "# Define the model\n",
+      "model = torch.nn.Sequential(\n",
+      "    torch.nn.Linear(1, 1)\n",
+      ")\n",
+      "\n",
+      "# Define the loss\n",
+      "loss_fn = torch.nn.MSELoss()\n",
+      "\n",
+      "# Define the optimizer\n",
+      "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
+      "\n",
+      "# Define the data\n",
+      "x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])\n",
+      "y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])\n",
+      "\n",
+      "# Train the model\n",
+      "for epoch in range(1000):\n",
+      "    # Forward pass\n",
+      "    y_pred = model(x_data)\n",
+      "\n",
+      "    # Compute and print loss\n",
+      "    loss = loss_fn(y_pred, y_data)\n",
+      "    if (epoch+1) % 100 == 0:\n",
+      "        print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')\n",
+      "\n",
+      "    # Zero the gradients\n",
+      "    optimizer.zero_grad()\n",
+      "\n",
+      "    # Backward pass\n",
+      "    loss.backward()\n",
+      "\n",
+      "    # Update the weights\n",
+      "    optimizer.step()\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mEpoch 100: loss = 0.0013\n",
+      "Epoch 200: loss = 0.0007\n",
+      "Epoch 300: loss = 0.0004\n",
+      "Epoch 400: loss = 0.0002\n",
+      "Epoch 500: loss = 0.0001\n",
+      "Epoch 600: loss = 0.0001\n",
+      "Epoch 700: loss = 0.0000\n",
+      "Epoch 800: loss = 0.0000\n",
+      "Epoch 900: loss = 0.0000\n",
+      "Epoch 1000: loss = 0.0000\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The prediction for x = 5 is 10.0.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The prediction for x = 5 is 10.0.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"\"\"Understand, write a single neuron neural network in PyTorch.\n",
+    "Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.\n",
+    "Return prediction for x = 5\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb654671",
+   "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/agents/agent_toolkits/sql_database.ipynb

@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
+   "metadata": {},
+   "source": [
+    "# SQL Database Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/sqlite.html) and is designed to answer more general questions about a database, as well as recover from errors.\n",
+    "\n",
+    "Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!\n",
+    "\n",
+    "This uses the example Chinook database. To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "53422913-967b-4f2a-8022-00269c1be1b1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.agents import AgentExecutor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "toolkit = SQLDatabaseToolkit(db=db)\n",
+    "\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
+   "metadata": {},
+   "source": [
+    "## Example: describing a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mArtist, Invoice, Playlist, Genre, Album, PlaylistTrack, Track, InvoiceLine, MediaType, Employee, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the playlisttrack table\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Describe the playlisttrack table\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
+   "metadata": {},
+   "source": [
+    "## Example: describing a table, recovering from an error\n",
+    "\n",
+    "In this example, the agent tries to search for a table that doesn't exist, but finds the next best result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bea76658-a65b-47e2-b294-6d52c5556246",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the PlaylistSong table\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistSong\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mError: table_names {'PlaylistSong'} not found in database\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should check the spelling of the table\n",
+      "Action: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m The table is called PlaylistTrack\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Describe the playlistsong table\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
+   "metadata": {},
+   "source": [
+    "## Example: running queries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "17bea710-4a23-4de0-b48e-21d57be48293",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the relevant tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Invoice, Customer\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Customer\" (\n",
+      "\t\"CustomerId\" INTEGER NOT NULL, \n",
+      "\t\"FirstName\" NVARCHAR(40) NOT NULL, \n",
+      "\t\"LastName\" NVARCHAR(20) NOT NULL, \n",
+      "\t\"Company\" NVARCHAR(80), \n",
+      "\t\"Address\" NVARCHAR(70), \n",
+      "\t\"City\" NVARCHAR(40), \n",
+      "\t\"State\" NVARCHAR(40), \n",
+      "\t\"Country\" NVARCHAR(40), \n",
+      "\t\"PostalCode\" NVARCHAR(10), \n",
+      "\t\"Phone\" NVARCHAR(24), \n",
+      "\t\"Fax\" NVARCHAR(24), \n",
+      "\t\"Email\" NVARCHAR(60) NOT NULL, \n",
+      "\t\"SupportRepId\" INTEGER, \n",
+      "\tPRIMARY KEY (\"CustomerId\"), \n",
+      "\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Customer' LIMIT 3;\n",
+      "CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId\n",
+      "1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg@embraer.com.br 3\n",
+      "2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5\n",
+      "3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"Invoice\" (\n",
+      "\t\"InvoiceId\" INTEGER NOT NULL, \n",
+      "\t\"CustomerId\" INTEGER NOT NULL, \n",
+      "\t\"InvoiceDate\" DATETIME NOT NULL, \n",
+      "\t\"BillingAddress\" NVARCHAR(70), \n",
+      "\t\"BillingCity\" NVARCHAR(40), \n",
+      "\t\"BillingState\" NVARCHAR(40), \n",
+      "\t\"BillingCountry\" NVARCHAR(40), \n",
+      "\t\"BillingPostalCode\" NVARCHAR(10), \n",
+      "\t\"Total\" NUMERIC(10, 2) NOT NULL, \n",
+      "\tPRIMARY KEY (\"InvoiceId\"), \n",
+      "\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Invoice' LIMIT 3;\n",
+      "InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total\n",
+      "1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98\n",
+      "2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96\n",
+      "3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should query the Invoice and Customer tables to get the total sales per country.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT c.Country, SUM(i.Total) AS TotalSales FROM Invoice i INNER JOIN Customer c ON i.CustomerId = c.CustomerId GROUP BY c.Country ORDER BY TotalSales DESC LIMIT 10\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The customers from the USA spent the most, with a total of $523.06.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The customers from the USA spent the most, with a total of $523.06.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"List the total sales per country. Which country's customers spent the most?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Playlist, PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Playlist\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(120), \n",
+      "\tPRIMARY KEY (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Playlist' LIMIT 3;\n",
+      "PlaylistId Name\n",
+      "1 Music\n",
+      "2 Movies\n",
+      "3 TV Shows\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can use a SELECT statement to get the total number of tracks in each playlist.\n",
+      "Action: query_checker_sql_db\n",
+      "Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m\n",
+      "\n",
+      "SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m The query looks correct, I can now execute it.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name LIMIT 10\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('90’s Music', 1477), ('Brazilian Music', 39), ('Classical', 75), ('Classical 101 - Deep Cuts', 25), ('Classical 101 - Next Steps', 25), ('Classical 101 - The Basics', 25), ('Grunge', 15), ('Heavy Metal Classic', 26), ('Music', 6580), ('Music Videos', 1)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c7503b5-d9d9-4faa-b064-29fcdb5ff213",
+   "metadata": {},
+   "source": [
+    "## Recovering from an error\n",
+    "\n",
+    "In this example, the agent is able to recover from an error after initially trying to access an attribute (`Track.ArtistId`) which doesn't exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9fe4901e-f9e1-4022-b6bc-80e2b2d6a3a4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mMediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Artist, InvoiceLine, Track\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Artist\" (\n",
+      "\t\"ArtistId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(120), \n",
+      "\tPRIMARY KEY (\"ArtistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Artist' LIMIT 3;\n",
+      "ArtistId Name\n",
+      "1 AC/DC\n",
+      "2 Accept\n",
+      "3 Aerosmith\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"Track\" (\n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(200) NOT NULL, \n",
+      "\t\"AlbumId\" INTEGER, \n",
+      "\t\"MediaTypeId\" INTEGER NOT NULL, \n",
+      "\t\"GenreId\" INTEGER, \n",
+      "\t\"Composer\" NVARCHAR(220), \n",
+      "\t\"Milliseconds\" INTEGER NOT NULL, \n",
+      "\t\"Bytes\" INTEGER, \n",
+      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
+      "\tPRIMARY KEY (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
+      "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
+      "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Track' LIMIT 3;\n",
+      "TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice\n",
+      "1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99\n",
+      "2 Balls to the Wall 2 2 1 None 342562 5510424 0.99\n",
+      "3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"InvoiceLine\" (\n",
+      "\t\"InvoiceLineId\" INTEGER NOT NULL, \n",
+      "\t\"InvoiceId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
+      "\t\"Quantity\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"InvoiceLineId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'InvoiceLine' LIMIT 3;\n",
+      "InvoiceLineId InvoiceId TrackId UnitPrice Quantity\n",
+      "1 1 2 0.99 1\n",
+      "2 1 4 0.99 1\n",
+      "3 2 6 0.99 1\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should query the database to get the top 3 best selling artists.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mError: (sqlite3.OperationalError) no such column: Track.ArtistId\n",
+      "[SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3]\n",
+      "(Background on this error at: https://sqlalche.me/e/14/e3q8)\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should double check my query before executing it.\n",
+      "Action: query_checker_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m\n",
+      "\n",
+      "SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity \n",
+      "FROM Artist \n",
+      "INNER JOIN Track ON Artist.ArtistId = Track.ArtistId \n",
+      "INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId \n",
+      "GROUP BY Artist.Name \n",
+      "ORDER BY TotalQuantity DESC \n",
+      "LIMIT 3;\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Who are the top 3 best selling artists?\")"
+   ]
+  }
+ ],
+ "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/modules/agents/agent_toolkits/titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
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+825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
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+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
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+843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
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docs/modules/agents/agent_toolkits/vectorstore.ipynb

@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "18ada398-dce6-4049-9b56-fc0ede63da9c",
+   "metadata": {},
+   "source": [
+    "# Vectorstore Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eecb683b-3a46-4b9d-81a3-7caefbfec1a1",
+   "metadata": {},
+   "source": [
+    "## Create the Vectorstores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9bfd0ed8-a5eb-443e-8e92-90be8cabb0a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA\n",
+    "llm = OpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "345bb078-4ec1-4e3a-827b-cd238c49054d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "state_of_union_store = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f50eb82-e1a5-4252-8306-8ec1b478d9b4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")\n",
+    "docs = loader.load()\n",
+    "ruff_texts = text_splitter.split_documents(docs)\n",
+    "ruff_store = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4814175-964d-42f1-aa9d-22801ce1e912",
+   "metadata": {},
+   "source": [
+    "## Initalize Toolkit and Agent\n",
+    "\n",
+    "First, we'll create an agent with a single vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5b3b3206",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import (\n",
+    "    create_vectorstore_agent,\n",
+    "    VectorStoreToolkit,\n",
+    "    VectorStoreInfo,\n",
+    ")\n",
+    "vectorstore_info = VectorStoreInfo(\n",
+    "    name=\"state_of_union_address\",\n",
+    "    description=\"the most recent state of the Union adress\",\n",
+    "    vectorstore=state_of_union_store\n",
+    ")\n",
+    "toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)\n",
+    "agent_executor = create_vectorstore_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a38ad10",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f2f455c",
+   "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 I need to find the answer in the state of the union address\n",
+      "Action: state_of_union_address\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d61e1e63",
+   "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 I need to use the state_of_union_address_with_sources tool to answer this question.\n",
+      "Action: state_of_union_address_with_sources\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{\"answer\": \" Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\\n\", \"sources\": \"../../state_of_the_union.txt\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address? List the source.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca07707",
+   "metadata": {},
+   "source": [
+    "## Multiple Vectorstores\n",
+    "We can also easily use this initialize an agent with multiple vectorstores and use the agent to route between them. To do this. This agent is optimized for routing, so it is a different toolkit and initializer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c3209fd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import (\n",
+    "    create_vectorstore_router_agent,\n",
+    "    VectorStoreRouterToolkit,\n",
+    "    VectorStoreInfo,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "815c4f39-308d-4949-b992-1361036e6e09",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ruff_vectorstore_info = VectorStoreInfo(\n",
+    "    name=\"ruff\",\n",
+    "    description=\"Information about the Ruff python linting library\",\n",
+    "    vectorstore=ruff_store\n",
+    ")\n",
+    "router_toolkit = VectorStoreRouterToolkit(\n",
+    "    vectorstores=[vectorstore_info, ruff_vectorstore_info],\n",
+    "    llm=llm\n",
+    ")\n",
+    "agent_executor = create_vectorstore_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=router_toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71680984-edaf-4a63-90f5-94edbd263550",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3cd1bf3e-e3df-4e69-bbe1-71c64b1af947",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to use the state_of_union_address tool to answer this question.\n",
+      "Action: state_of_union_address\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c5998b8d",
+   "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 I need to find out what tool ruff uses to run over Jupyter Notebooks\n",
+      "Action: ruff\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "744e9b51-fbd9-4778-b594-ea957d0f3467",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses and if the president mentioned it in the state of the union.\n",
+      "Action: ruff\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out if the president mentioned nbQA in the state of the union.\n",
+      "Action: state_of_union_address\n",
+      "Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'No, the president did not mention nbQA in the state of the union.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92203aa9-f63a-4ce1-b562-fadf4474ad9d",
+   "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/modules/agents/agents.md

@@ -1,7 +1,7 @@
 # Agents
 
 Agents use an LLM to determine which actions to take and in what order.
-An action can either be using a tool and observing its output, or returning to the user.
+An action can either be using a tool and observing its output, or returning a response to the user.
 For a list of easily loadable tools, see [here](tools.md).
 Here are the agents available in LangChain.
 

docs/modules/agents/examples/agent_vectorstore.ipynb

@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "68b24990",
+   "metadata": {},
+   "source": [
+    "# Agents and Vectorstores\n",
+    "\n",
+    "This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n",
+    "\n",
+    "The reccomended method for doing so is to create a VectorDBQAChain and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b22020a",
+   "metadata": {},
+   "source": [
+    "## Create the Vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2e87c10a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA\n",
+    "llm = OpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f2675861",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "docsearch = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bc5403d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_of_union = VectorDBQA.from_chain_type(llm=llm, chain_type=\"stuff\", vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1431cded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "915d3ff3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "96a2edf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "ruff_texts = text_splitter.split_documents(docs)\n",
+    "ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")\n",
+    "ruff = VectorDBQA.from_chain_type(llm=llm, chain_type=\"stuff\", vectorstore=ruff_db)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71ecef90",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a6c031",
+   "metadata": {},
+   "source": [
+    "## Create the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "eb142786",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import things that are needed generically\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.tools import BaseTool\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain import LLMMathChain, SerpAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "850bc4e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"State of Union QA System\",\n",
+    "        func=state_of_union.run,\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\"\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name = \"Ruff QA System\",\n",
+    "        func=ruff.run,\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\"\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "fc47f230",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the agent. We will use the default agent type here.\n",
+    "# See documentation for a full list of options.\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "10ca2db8",
+   "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 I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "4e91b811",
+   "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 I need to find out the advantages of using ruff over flake8\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Why use ruff over flake8?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "787a9b5e",
+   "metadata": {},
+   "source": [
+    "## Use the Agent solely as a router"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9161ba91",
+   "metadata": {},
+   "source": [
+    "You can also set `return_direct=True` if you intend to use the agent as a router and just want to directly return the result of the VectorDBQaChain.\n",
+    "\n",
+    "Notice that in the above examples the agent did some extra work after querying the VectorDBQAChain. You can avoid that and just return the result directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f59b377e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"State of Union QA System\",\n",
+    "        func=state_of_union.run,\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
+    "        return_direct=True\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name = \"Ruff QA System\",\n",
+    "        func=ruff.run,\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
+    "        return_direct=True\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "8615707a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "36e718a9",
+   "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 I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What did biden say about ketanji brown jackson in the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "edfd0a1a",
+   "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 I need to find out the advantages of using ruff over flake8\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Why use ruff over flake8?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49a0cbbe",
+   "metadata": {},
+   "source": [
+    "## Multi-Hop vectorstore reasoning\n",
+    "\n",
+    "Because vectorstores are easily usable as tools in agents, it is easy to use answer multi-hop questions that depend on vectorstores using the existing agent framework"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d397a233",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"State of Union QA System\",\n",
+    "        func=state_of_union.run,\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name = \"Ruff QA System\",\n",
+    "        func=ruff.run,\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "06157240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the agent. We will use the default agent type here.\n",
+    "# See documentation for a full list of options.\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "b492b520",
+   "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 I need to find out what tool ruff uses to run over Jupyter Notebooks, and if the president mentioned it in the state of the union.\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to find out if the president mentioned this tool in the state of the union.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'No, the president did not mention nbQA in the state of the union.'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3b857d6",
+   "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/agents/examples/async_agent.ipynb

@@ -403,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/examples/load_from_hub.ipynb

@@ -29,16 +29,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m Yes.\n",
-      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
-      "Follow up: What is Rafael Nadal's hometown?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001b[0m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
+      "Intermediate answer: \u001B[36;1m\u001B[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
+      "Follow up: What is Rafael Nadal's hometown?\u001B[0m\n",
+      "Intermediate answer: \u001B[36;1m\u001B[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/agents/how_to_guides.rst

@@ -1,12 +1,23 @@
 How-To Guides
 =============
 
+There are three types of examples in this section:
+
+1. Agent Overview: how-to-guides for generic agent functionality
+2. Agent Toolkits: how-to-guides for specific agent toolkits (agents optimized for interacting with a certain resource)
+3. Agent Types: how-to-guides for working with the different agent types
+
+Agent Overview
+---------------
+
 The first category of how-to guides here cover specific parts of working with agents.
 
 `Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.
 
 `Custom Tools <./examples/custom_tools.html>`_: How to create custom tools that an agent can use.
 
+`Agents With Vectorstores <./examples/agent_vectorstore.html>`_: How to use vectorstores with agents.
+
 `Intermediate Steps <./examples/intermediate_steps.html>`_: How to access and use intermediate steps to get more visibility into the internals of an agent.
 
 `Custom Agent <./examples/custom_agent.html>`_: How to create a custom agent (specifically, a custom LLM + prompt to drive that agent).
@@ -19,7 +30,48 @@ The first category of how-to guides here cover specific parts of working with ag
 
 `Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.
 
-The next set of examples are all end-to-end agents for specific applications.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   ./examples/*
+
+
+Agent Toolkits
+---------------
+
+The next set of examples covers agents with toolkits.
+As opposed to the examples above, these examples are not intended to show off an agent `type`,
+but rather to show off an agent applied to particular use case.
+
+`SQLDatabase Agent <./agent_toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.
+
+`JSON Agent <./agent_toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.
+
+`OpenAPI Agent <./agent_toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.
+
+`VectorStore Agent <./agent_toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.
+
+`Python Agent <./agent_toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.
+
+`Pandas DataFrame Agent <./agent_toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
+
+`CSV Agent <./agent_toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   ./agent_toolkits/*
+
+
+Agent Types
+---------------
+
+The final set of examples are all end-to-end example of different agent types.
 In all examples there is an Agent with a particular set of tools.
 
 - Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <../chains.html>`_ documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see `this documentation <./tools.html>`_
@@ -49,16 +101,13 @@ In all examples there is an Agent with a particular set of tools.
 
 
 
-.. toctree::
-   :maxdepth: 1
-   :glob:
-   :hidden:
 
-   ./examples/*
 
 .. toctree::
    :maxdepth: 1
    :glob:
    :hidden:
 
-   ./implementations/*
+   ./implementations/*
+
+

docs/modules/agents/implementations/mrkl.ipynb

@@ -205,7 +205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/implementations/self_ask_with_search.ipynb

@@ -63,7 +63,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -77,7 +77,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/key_concepts.md

@@ -8,3 +8,8 @@ For more detailed information on agents, and different types of agents in LangCh
 Tools are functions that agents can use to interact with the world.
 These tools can be generic utilities (e.g. search), other chains, or even other agents.
 For more detailed information on tools, and different types of tools in LangChain, see [this documentation](tools.md).
+
+## ToolKits
+Toolkits are groups of tools that are best used together.
+They allow you to logically group and initialize a set of tools that share a particular resource (such as a database connection or json object). 
+They can be used to construct an agent for a specific use-case.

docs/modules/audio_models/examples/bananadev.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bananadev\n",
+    "\n",
+    "This notebook covers how to use whisper from banana.dev, build an audio chain and deploy it in combination with llm to summarize a transcript gathered from an audio file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langchain\n",
+    "from langchain.audio_models import AudioBanana\n",
+    "from langchain.cache import SQLiteCache\n",
+    "from langchain.chains import AudioChain, LLMChain, SimpleSequentialChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting the cache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_cache = SQLiteCache(database_path=\".langchain.db\")\n",
+    "\n",
+    "langchain.llm_cache = audio_cache"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the audio model\n",
+    "\n",
+    "An `AudioBanana` object has the following arguments:\n",
+    "\n",
+    "* `model_key` (str): model endpoint to use;\n",
+    "* `banana_api_key`(optional[str]): banana api key:\n",
+    "* `max_chars` (optional[int]): max number of chars to return.\n",
+    "\n",
+    "An `AudioChain` object has the following arguments:\n",
+    "* `audio_model` (AudioBase): the audio model to use;\n",
+    "* `output_key` (str): the task to be performed by the model. Not all the audio models support all the tasks. In the case of AudioBanana only `\"transcribe\"` is a valid output key. The output key will returned along with the text data gathered from the audio file.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_model = AudioBanana(model_key=\"[YOUR MODEL KEY]\", max_chars=20000)\n",
+    "\n",
+    "audio_chain = AudioChain(audio_model=audio_model, output_key=\"transcribe\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up a llm and an LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0.7)\n",
+    "template = \"\"\"Speech: {text}\n",
+    "\n",
+    "    Write a short 3 sentence summary of the speech.\n",
+    "\n",
+    "    Summary:\"\"\"\n",
+    "\n",
+    "# note how the input variabòes\n",
+    "prompt_template = PromptTemplate(input_variables=[\"text\"], template=template)\n",
+    "\n",
+    "summary_chain = LLMChain(llm=llm, prompt=prompt_template)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining a SimpleSequentialChain and running"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "speech_summary_chain = SimpleSequentialChain(\n",
+    "    chains=[audio_chain, summary_chain], verbose=True\n",
+    ")\n",
+    "\n",
+    "audio_path = \"example_data/ihaveadream.mp3\"\n",
+    "speech_summary_chain.run(audio_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/audio_models/examples/whisper.ipynb

@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Whisper\n",
+    "\n",
+    "This notebook covers how to use whisper from openAI, build an audio chain and deploy it in combination with llm to summarize a transcript gathered from an audio file. A translation task is also shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.audio_models import Whisper\n",
+    "from langchain.chains import AudioChain, LLMChain, SimpleSequentialChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transcript task"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the audio model\n",
+    "\n",
+    "A `Whisper` objects has the following arguments:\n",
+    "\n",
+    "* `model_key` (str): model endpoint to use;\n",
+    "* `prompt` (optional[str]): openAi's whisper allows prompt to make the model context-aware and better transcribe/translate words;\n",
+    "* `language` (optional[str]): to add only in the case of transcript as a task;\n",
+    "* `max_chars` (optional[int]): max number of chars to return;\n",
+    "* `model` (optional[str]): name of the model to use, set to `whisper-1` by default;\n",
+    "* `temperature` (Optional[float]): temperature;\n",
+    "* `response format` (optional[str]): response format, set by default to `json`\n",
+    "\n",
+    "An `AudioChain` object has the following arguments:\n",
+    "* `audio_model` (AudioBase): the audio model to use;\n",
+    "* `output_key` (str): the task to be performed by the model. Not all the audio models support all the tasks. In the case of Whisper, the possible tasks are `\"transcribe\"` and `\"translate\"`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_model = Whisper(model_key=\"YOUR_OPENAI_KEY\", max_chars=20000)\n",
+    "audio_chain = AudioChain(audio_model=audio_model, output_key=\"transcribe\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up llm and an LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(\n",
+    "    openai_api_key=\"YOUR_OPENAI_KEY\",\n",
+    "    temperature=0.7,\n",
+    ")\n",
+    "\n",
+    "template = \"\"\"Speech: {text}\n",
+    "Write a short 3 sentence summary of the speech.\n",
+    "Summary:\"\"\"\n",
+    "\n",
+    "prompt_template = PromptTemplate(input_variables=[\"text\"], template=template)\n",
+    "\n",
+    "summary_chain = LLMChain(llm=llm, prompt=prompt_template)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining a SimpleSequentialChain and running"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "speech_to_summary_chain = SimpleSequentialChain(\n",
+    "    chains=[audio_chain, summary_chain], verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3mDo it! Just do it! Don't let your dreams be dreams. Yesterday, you said tomorrow. So just do it! Make your dreams come true! Just do it! Some people dream of success while you're going to wake up and work hard at it. Nothing is impossible! You should get to the point where anyone else would quit, and you're not going to stop there! No! What are you waiting for? Do it! Just do it! Yes you can! Just do it! If you're tired of starting over, stop giving up!\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m The speaker encourages the audience to take action and pursue their dreams. They emphasize that nothing is impossible and that the only way to make their dreams come true is to work hard and not give up. They urge the audience to act now and not wait any longer.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' The speaker encourages the audience to take action and pursue their dreams. They emphasize that nothing is impossible and that the only way to make their dreams come true is to work hard and not give up. They urge the audience to act now and not wait any longer.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "audio_path = \"example_data/doit.mp3\"\n",
+    "speech_to_summary_chain.run(audio_path)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Translate\n",
+    "To perform a translation, we just make few changes to the above workflow."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### New AudioChain with output key set to \"translation\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_chain = AudioChain(audio_model=audio_model, output_key=\"translate\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### New SimpleSequentialChain with the new AudioChain and run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "translation_to_summary_chain = SimpleSequentialChain(\n",
+    "    chains=[audio_chain, summary_chain], verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3mI wanted a chicken! Ah, this is the chicken! I like this, I hadn't seen it before! A CHICKEN! I'm afraid of getting my hands dirty and not playing the guitar anymore, otherwise I would break it. Look, a chicken, really, beautiful!\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m The speaker is excited to have found a chicken, noting that they have not seen it before. They express fear at getting their hands dirty, as it could prevent them from playing the guitar. Finally, they express admiration for the chicken's beauty.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" The speaker is excited to have found a chicken, noting that they have not seen it before. They express fear at getting their hands dirty, as it could prevent them from playing the guitar. Finally, they express admiration for the chicken's beauty.\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "audio_path = \"example_data/pollo.mp3\"\n",
+    "translation_to_summary_chain.run(audio_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/chains.rst

@@ -2,8 +2,8 @@ Chains
 ==========================
 
 Using an LLM in isolation is fine for some simple applications,
-but many more complex ones require chaining LLMs - either with eachother or with other experts.
-LangChain provides a standard interface for Chains, as well as some common implementations of chains for easy use.
+but many more complex ones require chaining LLMs - either with each other or with other experts.
+LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
 
 The following sections of documentation are provided:
 
@@ -26,4 +26,4 @@ The following sections of documentation are provided:
    ./chains/getting_started.ipynb
    ./chains/how_to_guides.rst
    ./chains/key_concepts.rst
-   Reference<../reference/modules/chains.rst>
+   Reference<../reference/modules/chains.rst>

docs/modules/chains/async_chain.ipynb

@@ -39,7 +39,7 @@
       "\n",
       "\n",
       "SparkleSmile Toothpaste\n",
-      "\u001b[1mConcurrent executed in 1.54 seconds.\u001b[0m\n",
+      "\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
       "\n",
       "\n",
       "BrightSmile Toothpaste Co.\n",
@@ -55,7 +55,7 @@
       "\n",
       "\n",
       "BrightSmile Toothpaste.\n",
-      "\u001b[1mSerial executed in 6.38 seconds.\u001b[0m\n"
+      "\u001B[1mSerial executed in 6.38 seconds.\u001B[0m\n"
      ]
     }
    ],

docs/modules/chains/examples/pal.ipynb

@@ -71,17 +71,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mdef solution():\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mdef solution():\n",
       "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
       "    cindy_pets = 4\n",
       "    marcia_pets = cindy_pets + 2\n",
       "    jan_pets = marcia_pets * 3\n",
       "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
       "    result = total_pets\n",
-      "    return result\u001b[0m\n",
+      "    return result\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -139,8 +139,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -151,9 +151,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished PALChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -212,8 +212,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -224,9 +224,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     }
    ],

docs/modules/chains/examples/sqlite.ipynb

@@ -379,9 +379,17 @@
       ")\n",
       "\n",
       "SELECT * FROM 'Track' LIMIT 2;\n",
-      "TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice\n",
-      "1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99\n",
-      "2 Balls to the Wall 2 2 1 None 342562 5510424 0.99\n"
+      "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
+      "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
+      "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jon/projects/langchain/langchain/sql_database.py:121: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "  sample_rows = connection.execute(command)\n"
      ]
     }
    ],
@@ -434,6 +442,131 @@
     "db_chain.run(\"What are some example tracks by Bach?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ef94e948",
+   "metadata": {},
+   "source": [
+    "### Custom Table Info\n",
+    "In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first `sample_rows_in_table_info` sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns. \n",
+    "\n",
+    "This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let's provide a custom definition and sample rows for the Track table with only a few columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2ad33ab1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "custom_table_info = {\n",
+    "    \"Track\": \"\"\"CREATE TABLE Track (\n",
+    "\t\"TrackId\" INTEGER NOT NULL, \n",
+    "\t\"Name\" NVARCHAR(200) NOT NULL,\n",
+    "\t\"Composer\" NVARCHAR(220),\n",
+    "\tPRIMARY KEY (\"TrackId\")\n",
+    ")\n",
+    "\n",
+    "SELECT * FROM 'Track' LIMIT 3;\n",
+    "TrackId\tName\tComposer\n",
+    "1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
+    "2\tBalls to the Wall\tNone\n",
+    "3\tMy favorite song ever\tThe coolest composer of all time\"\"\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "db144352",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "CREATE TABLE \"Playlist\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(120), \n",
+      "\tPRIMARY KEY (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Playlist' LIMIT 2;\n",
+      "PlaylistId\tName\n",
+      "1\tMusic\n",
+      "2\tMovies\n",
+      "\n",
+      "CREATE TABLE Track (\n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(200) NOT NULL,\n",
+      "\t\"Composer\" NVARCHAR(220),\n",
+      "\tPRIMARY KEY (\"TrackId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Track' LIMIT 3;\n",
+      "TrackId\tName\tComposer\n",
+      "1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
+      "2\tBalls to the Wall\tNone\n",
+      "3\tMy favorite song ever\tThe coolest composer of all time\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = SQLDatabase.from_uri(\n",
+    "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
+    "    include_tables=['Track', 'Playlist'],\n",
+    "    sample_rows_in_table_info=2,\n",
+    "    custom_table_info=custom_table_info)\n",
+    "\n",
+    "print(db.table_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc6f507",
+   "metadata": {},
+   "source": [
+    "Note how our custom table definition and sample rows for `Track` overrides the `sample_rows_in_table_info` parameter. Tables that are not overriden by `custom_table_info`, in this example `Playlist`, will have their table info gathered automatically as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dfbda4e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
+      "What are some example tracks by Bach? \n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Some example tracks by Bach are \\'American Woman\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\', and \\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\'.'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
+    "db_chain.run(\"What are some example tracks by Bach?\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c12ae15a",
@@ -542,7 +675,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/from_hub.ipynb

@@ -34,10 +34,10 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "whats 2 raised to .12\u001b[32;1m\u001b[1;3m\n",
-      "Answer: 1.0791812460476249\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Entering new LLMMathChain chain...\u001B[0m\n",
+      "whats 2 raised to .12\u001B[32;1m\u001B[1;3m\n",
+      "Answer: 1.0791812460476249\u001B[0m\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/chains/generic/llm_chain.ipynb

@@ -42,13 +42,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -95,11 +95,11 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
+      "\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/chains/generic/serialization.ipynb

@@ -136,13 +136,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -319,13 +319,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
       "\n",
-      "Answer: Let's think step by step.\u001b[0m\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/chains/getting_started.ipynb

@@ -9,13 +9,13 @@
     "In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.\n",
     "\n",
     "In this tutorial, we will cover:\n",
-    "- Using the simple LLM chain\n",
+    "- Using a simple LLM chain\n",
     "- Creating sequential chains\n",
     "- Creating a custom chain\n",
     "\n",
     "## Why do we need chains?\n",
     "\n",
-    "Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, format it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
+    "Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
    ]
   },
   {
@@ -88,7 +88,7 @@
    "source": [
     "## Combine chains with the `SequentialChain`\n",
     "\n",
-    "The next step after calling a language model is make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.\n",
+    "The next step after calling a language model is to make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest type of a sequential chain, where each step has a single input/output, and the output of one step is the input to the next.\n",
     "\n",
     "In this tutorial, our sequential chain will:\n",
     "1. First, create a company name for a product. We will reuse the `LLMChain` we'd previously initialized to create this company name.\n",
@@ -156,7 +156,7 @@
    "source": [
     "## Create a custom chain with the `Chain` class\n",
     "\n",
-    "LangChain provides many chains out of the box, but sometimes you may want to create a custom chains for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
+    "LangChain provides many chains out of the box, but sometimes you may want to create a custom chain for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
     "\n",
     "In order to create a custom chain:\n",
     "1. Start by subclassing the `Chain` class,\n",

docs/modules/document_loaders/examples/CoNLL-U.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9f98a15e",
+   "metadata": {},
+   "source": [
+    "# CoNLL-U\n",
+    "This is an example of how to load a file in [CoNLL-U](https://universaldependencies.org/format.html) format. The whole file is treated as one document. The example data (`conllu.conllu`) is based on one of the standard UD/CoNLL-U examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9b2e33e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import CoNLLULoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b5eec48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = CoNLLULoader(\"example_data/conllu.conllu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10f3f725",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acbb3579",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/document_loaders/examples/audio_files.ipynb

@@ -0,0 +1,98 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audio Files\n",
+    "\n",
+    "This notebook shows how to add data from audio files into a format suitable by LangChain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AudioLoader\n",
+    "from langchain.audio_models import Whisper"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An `AudioLoader` object has as parameters:\n",
+    "\n",
+    "* `audio_model` (AudioBase): the audio model to use;\n",
+    "* `file_path` (str): the file path;\n",
+    "* `task` (str): the task to perform. Depending on the model, this can be `transcribe` or `translate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_model = Whisper(model_key=\"[YOUR_OPENAI_API_KEY]\", max_chars=20000)\n",
+    "\n",
+    "file_path = r\"example_data\\pasolini.mp3\"\n",
+    "loader = AudioLoader(audio_model=audio_model, file_path=file_path, task=\"translate\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`AudioLoader.load()` loads the data into a `Document` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='I think that fascism, the fascist regime, was not built on the conclusion that a group of criminals has power. And this group of criminals has power, but it has not been able to do anything. It has not been able to even remotely change the reality of Italy. Now, on the contrary, the regime is a democratic regime, etc. But that acculturation, that homologation that fascism has not been able to obtain, the power of today, that is, the power of consumerism, instead, it is able to obtain it perfectly. I can tell you without a doubt that true fascism is precisely this power of consumerism that is destroying Italy. And this thing happened so quickly that we did not realize it. It has happened in the last 5, 6, 7, 10 years. It was a kind of nightmare in which we saw Italy around us destroy, disappear, disappear. Now, perhaps awakening from this nightmare and looking around, we realize that there is nothing more to do.', lookup_str='', metadata={'source': 'docs\\\\modules\\\\document_loaders\\\\examples\\\\example_data\\\\pasolini.mp3', 'task': 'translate'}, lookup_index=0)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/document_loaders/examples/copypaste.ipynb

@@ -0,0 +1,102 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d9826810",
+   "metadata": {},
+   "source": [
+    "# Copy Paste\n",
+    "\n",
+    "This notebook covers how to load a document object from something you just want to copy and paste. In this case, you don't even need to use a DocumentLoader, but rather can just construct the Document directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd9e71a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f40d3f30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"..... put the text you copy pasted here......\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d409bdba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc = Document(page_content=text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc0eff72",
+   "metadata": {},
+   "source": [
+    "## Metadata\n",
+    "If you want to add metadata about the where you got this piece of text, you easily can with the metadata key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe3aa5aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata = {\"source\": \"internet\", \"date\": \"Friday\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "827d4e91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc = Document(page_content=text, metadata=metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c986a43d",
+   "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/document_loaders/examples/example_data/conllu.conllu

@@ -0,0 +1,8 @@
+# sent_id = 1
+# text = They buy and sell books.
+1	They	they	PRON	PRP	Case=Nom|Number=Plur	2	nsubj	2:nsubj|4:nsubj	_
+2	buy	buy	VERB	VBP	Number=Plur|Person=3|Tense=Pres	0	root	0:root	_
+3	and	and	CONJ	CC 	_	4	cc	4:cc	_
+4	sell	sell	VERB	VBP	Number=Plur|Person=3|Tense=Pres	2	conj	0:root|2:conj	_
+5	books	book	NOUN	NNS	Number=Plur	2	obj	2:obj|4:obj	SpaceAfter=No
+6	.	.	PUNCT	.	_	2	punct	2:punct	_

docs/modules/document_loaders/examples/image.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f70e6118",
+   "metadata": {},
+   "source": [
+    "# Images\n",
+    "\n",
+    "This covers how to load images such as JPGs PNGs into a document format that we can use downstream."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09d64998",
+   "metadata": {},
+   "source": [
+    "## Using Unstructured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0cc0cd42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.image import UnstructuredImageLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "082d557c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = UnstructuredImageLoader(\"layout-parser-paper-fast.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "df11c953",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4284d44c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content=\"LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\n\\n\\n‘Zxjiang Shen' (F3}, Ruochen Zhang”, Melissa Dell*, Benjamin Charles Germain\\nLeet, Jacob Carlson, and Weining LiF\\n\\n\\nsugehen\\n\\nshangthrows, et\\n\\n“Abstract. Recent advanocs in document image analysis (DIA) have been\\n‘pimarliy driven bythe application of neural networks dell roar\\n{uteomer could be aly deployed in production and extended fo farther\\n[nvetigtion. However, various factory ke lcely organize codebanee\\nsnd sophisticated modal cnigurations compat the ey ree of\\n‘erin! innovation by wide sence, Though there have been sng\\n‘Hors to improve reuablty and simplify deep lees (DL) mode\\n‘aon, sone of them ae optimized for challenge inthe demain of DIA,\\nThis roprscte a major gap in the extng fol, sw DIA i eal to\\nscademic research acon wie range of dpi in the social ssencee\\n[rary for streamlining the sage of DL in DIA research and appicn\\n‘tons The core LayoutFaraer brary comes with a sch of simple and\\nIntative interfaee or applying and eutomiing DI. odel fr Inyo de\\npltfom for sharing both protrined modes an fal document dist\\n{ation pipeline We demonutate that LayootPareer shea fr both\\nlightweight and lrgeseledgtieation pipelines in eal-word uae ces\\nThe leary pblely smal at Btspe://layost-pareergsthab So\\n\\n\\n\\n‘Keywords: Document Image Analysis» Deep Learning Layout Analysis\\n‘Character Renguition - Open Serres dary « Tol\\n\\n\\nIntroduction\\n\\n\\n‘Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndoctiment image analysis (DIA) tea including document image clasiffeation [I]\\n\", lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg'}, lookup_index=0)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09957371",
+   "metadata": {},
+   "source": [
+    "### Retain Elements\n",
+    "\n",
+    "Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0fab833b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = UnstructuredImageLoader(\"layout-parser-paper-fast.jpg\", mode=\"elements\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c3e8ff1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43c23d2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg', 'filename': 'layout-parser-paper-fast.jpg', 'page_number': 1, 'category': 'Title'}, lookup_index=0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0]"
+   ]
+  }
+ ],
+ "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/document_loaders/how_to_guides.rst

@@ -57,6 +57,10 @@ There are a lot of different document loaders that LangChain supports. Below are
 
 `Online PDF <./examples/online_pdf.html>`_: A walkthrough of how to load data from an online PDF.
 
+`CoNLL-U <./examples/CoNLL-U.html>`_: A walkthrough of how to load data from a ConLL-U file.
+
+`iFixit <./examples/ifixit.html>`_: A walkthrough of how to search and load data like guides, technical Q&A's, and device wikis from iFixit.com
+
 .. toctree::
    :maxdepth: 1
    :glob:

docs/modules/indexes/chain_examples/chat_vector_db.ipynb

@@ -268,12 +268,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fb44daa",
-   "metadata": {},
+   "source": [
+    "## Chat Vector DB with `search_distance`\n",
+    "If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "vectordbkwargs = {\"search_distance\": 0.9}"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, 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})"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
     "## Chat Vector DB with `map_reduce`\n",
     "We can also use different types of combine document chains with the Chat Vector DB chain."
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -486,7 +522,7 @@
    "source": [
     "chat_history = [(query, result[\"answer\"])]\n",
     "query = \"Did he mention who she suceeded\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+    "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
    ]
   }
  ],

docs/modules/indexes/chain_examples/graph_qa.ipynb

@@ -178,16 +178,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new GraphQAChain chain...\u001B[0m\n",
       "Entities Extracted:\n",
-      "\u001b[32;1m\u001b[1;3m Intel\u001b[0m\n",
+      "\u001B[32;1m\u001B[1;3m Intel\u001B[0m\n",
       "Full Context:\n",
-      "\u001b[32;1m\u001b[1;3mIntel is going to build $20 billion semiconductor \"mega site\"\n",
+      "\u001B[32;1m\u001B[1;3mIntel is going to build $20 billion semiconductor \"mega site\"\n",
       "Intel is building state-of-the-art factories\n",
       "Intel is creating 10,000 new good-paying jobs\n",
-      "Intel is helping build Silicon Valley\u001b[0m\n",
+      "Intel is helping build Silicon Valley\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/indexes/chain_examples/question_answering.ipynb

@@ -30,29 +30,24 @@
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores import Chroma\n",
     "from langchain.docstore.document import Document\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.indexes.vectorstore import VectorstoreIndexCreator"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "291f0117",
+   "id": "ef9305cc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
+    "index_creator = VectorstoreIndexCreator()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "fd9666a9",
+   "execution_count": 3,
+   "id": "291f0117",
    "metadata": {},
    "outputs": [
     {
@@ -65,12 +60,14 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_documents(texts, embeddings)"
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "docsearch = index_creator.from_loaders([loader])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "d1eaf6e6",
    "metadata": {},
    "outputs": [],

docs/modules/indexes/examples/textsplitter.ipynb

@@ -43,7 +43,7 @@
    "metadata": {},
    "source": [
     "## Generic Recursive Text Splitting\n",
-    "This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `[\"\\n\\n\", \"\\n\", \" \", \"\"]`. This has the affect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.\n",
+    "This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `[\"\\n\\n\", \"\\n\", \" \", \"\"]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.\n",
     "\n",
     "\n",
     "1. How the text is split: by list of characters\n",

docs/modules/indexes/examples/vectorstores.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "#  VectorStores\n",
     "\n",
-    "This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefor, it is recommended that you familiarize yourself with the [embedding notebook](embeddings.ipynb) before diving into this.\n",
+    "This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [embedding notebook](embeddings.ipynb) before diving into this.\n",
     "\n",
     "This covers generic high level functionality related to all vector stores. For guides on specific vectorstores, please see the how-to guides [here](../how_to_guides.rst)"
    ]

docs/modules/indexes/getting_started.ipynb

@@ -2,45 +2,204 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "07c1e3b9",
+   "id": "2244801b",
    "metadata": {},
    "source": [
     "# Getting Started\n",
     "\n",
-    "This example showcases question answering over a vector database.\n",
-    "We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain."
+    "This example showcases question answering over documents.\n",
+    "We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain.\n",
+    "\n",
+    "Question answering over documents consists of three steps:\n",
+    "\n",
+    "1. Create an index\n",
+    "2. Create a question answering chain\n",
+    "3. Ask questions!\n",
+    "\n",
+    "Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.\n",
+    "\n",
+    "First, let's import some common classes we'll use no matter what."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "82525493",
+   "id": "8d369452",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain import OpenAI, VectorDBQA"
+    "from langchain.chains import VectorDBQA\n",
+    "from langchain.llms import OpenAI"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0b7adc54",
+   "id": "07c1e3b9",
    "metadata": {},
    "source": [
-    "Here we load in the documents we want to use to create our index."
+    "Next in the generic setup, let's specify the document loader we want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "33958a86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../state_of_the_union.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "489c74bb",
+   "metadata": {},
+   "source": [
+    "## One Line Index Creation\n",
+    "\n",
+    "To get started as quickly as possible, we can use the `VectorstoreIndexCreator`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "611e0c19",
+   "id": "403fc231",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.indexes import VectorstoreIndexCreator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "57a8a199",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "index = VectorstoreIndexCreator().from_loaders([loader])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3493fa4",
+   "metadata": {},
+   "source": [
+    "Now that the index is created, we can use it to ask questions of the data! Note that under the hood this is actually doing a few steps as well, which we will cover later in this guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "23d0d234",
+   "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, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "index.query(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae46b239",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'What did the president say about Ketanji Brown Jackson',\n",
+       " 'answer': \" The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\\n\",\n",
+       " 'sources': '../state_of_the_union.txt'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "index.query_with_sources(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff100212",
+   "metadata": {},
+   "source": [
+    "What is returned from the `VectorstoreIndexCreator` is `VectorStoreIndexWrapper`, which provides these nice `query` and `query_with_sources` functionality. If we just wanted to access the vectorstore directly, we can also do that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b04f3c10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<langchain.vectorstores.chroma.Chroma at 0x113a3a700>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index.vectorstore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cb6d2eb",
+   "metadata": {},
+   "source": [
+    "## Walkthrough\n",
+    "\n",
+    "Okay, so what's actually going on? How is this index getting created?\n",
+    "\n",
+    "A lot of the magic is being hid in this `VectorstoreIndexCreator`. What is this doing?\n",
+    "\n",
+    "There are three main steps going on after the documents are loaded:\n",
+    "\n",
+    "1. Splitting documents into chunks\n",
+    "2. Creating embeddings for each document\n",
+    "3. Storing documents and embeddings in a vectorstore\n",
+    "\n",
+    "Let's walk through this in code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "54270abc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../state_of_the_union.txt')\n",
     "documents = loader.load()"
    ]
   },
@@ -54,11 +213,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "afecb8cf",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.text_splitter import CharacterTextSplitter\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)"
    ]
@@ -73,11 +233,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "9eaaa735",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
     "embeddings = OpenAIEmbeddings()"
    ]
   },
@@ -91,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "5c7049db",
    "metadata": {},
    "outputs": [
@@ -105,6 +266,7 @@
     }
    ],
    "source": [
+    "from langchain.vectorstores import Chroma\n",
     "db = Chroma.from_documents(texts, embeddings)"
    ]
   },
@@ -113,12 +275,13 @@
    "id": "30c4e5c6",
    "metadata": {},
    "source": [
-    "Finally, we create a chain and use it to answer questions!"
+    "So that's creating the index.\n",
+    "Then, as before, we create a chain and use it to answer questions!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "3018f865",
    "metadata": {},
    "outputs": [],
@@ -128,17 +291,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "032a47f8",
    "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, a former federal public defender, and from a family of public school educators and police officers. He said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+       "\" The President said that Ketanji Brown Jackson is one of the nation's top legal minds and a consensus builder, with a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. She is a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers.\""
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -148,10 +311,40 @@
     "qa.run(query)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9464690e",
+   "metadata": {},
+   "source": [
+    "`VectorstoreIndexCreator` is just a wrapper around all this logic. It is configurable in the text splitter it uses, the embeddings it uses, and the vectorstore it uses. For example, you can configure it as below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4001bbc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_creator = VectorstoreIndexCreator(\n",
+    "    vectorstore_cls=Chroma, \n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    "    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78d8d143",
+   "metadata": {},
+   "source": [
+    "Hopefully this highlights what is going on under the hood of `VectorstoreIndexCreator`. While we think it's important to have a simple way to create indexes, we also think it's important to understand what's going on under the hood."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b403637",
+   "id": "dd7257bf",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/indexes/how_to_guides.rst

@@ -36,6 +36,8 @@ In the below guides, we cover different types of vectorstores and how to use the
 
 `Chroma <./vectorstore_examples/chroma.html>`_: A walkthrough of how to use the Chroma vectorstore wrapper.
 
+`DeepLake <./vectorstore_examples/deeplake.html>`_: A walkthrough of how to use the Deep Lake, data lake, wrapper.
+
 `FAISS <./vectorstore_examples/faiss.html>`_: A walkthrough of how to use the FAISS vectorstore wrapper.
 
 `Elastic Search <./vectorstore_examples/elasticsearch.html>`_: A walkthrough of how to use the ElasticSearch wrapper.

docs/modules/indexes/vectorstore_examples/atlas.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# AtlasDB\n",
+    "\n",
+    "This notebook shows you how to use functionality related to the AtlasDB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import SpacyTextSplitter\n",
+    "from langchain.vectorstores import AtlasDB\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting en-core-web-sm==3.5.0\n",
+      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl (12.8 MB)\n",
+      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m12.8/12.8 MB\u001B[0m \u001B[31m90.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m00:01\u001B[0m00:01\u001B[0m\n",
+      "\u001B[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from en-core-web-sm==3.5.0) (3.5.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (23.0)\n",
+      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.1.1)\n",
+      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.3.0)\n",
+      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.4.5)\n",
+      "Requirement already satisfied: pathy>=0.10.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.10.1)\n",
+      "Requirement already satisfied: setuptools in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (67.4.0)\n",
+      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.64.1)\n",
+      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.4)\n",
+      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (6.3.0)\n",
+      "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.7)\n",
+      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.7)\n",
+      "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.0)\n",
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+      "Requirement already satisfied: jinja2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.1.2)\n",
+      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.10.5)\n",
+      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.8)\n",
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+      "Requirement already satisfied: numpy>=1.15.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.24.2)\n",
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+      "Requirement already satisfied: typing-extensions>=4.2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.5.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.1)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2022.12.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.26.14)\n",
+      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from thinc<8.2.0,>=8.1.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.9)\n",
+      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from thinc<8.2.0,>=8.1.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.0.4)\n",
+      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.1.2)\n",
+      "\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.0.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
+      "\u001B[38;5;2m✔ Download and installation successful\u001B[0m\n",
+      "You can now load the package via spacy.load('en_core_web_sm')\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python -m spacy download en_core_web_sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ATLAS_TEST_API_KEY = '7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = SpacyTextSplitter(separator='|')\n",
+    "texts = []\n",
+    "for doc in text_splitter.split_documents(documents):\n",
+    "    texts.extend(doc.page_content.split('|'))\n",
+    "                 \n",
+    "texts = [e.strip() for e in texts]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-24 16:13:49.696 | INFO     | nomic.project:_create_project:884 - Creating project `test_index_1677255228.136989` in organization `Atlas Demo`\n",
+      "2023-02-24 16:13:51.087 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:51.225 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:51.481 | INFO     | nomic.project:add_text:1351 - Uploading text to Atlas.\n",
+      "1it [00:00,  1.20it/s]\n",
+      "2023-02-24 16:13:52.318 | INFO     | nomic.project:add_text:1422 - Text upload succeeded.\n",
+      "2023-02-24 16:13:52.628 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:53.380 | INFO     | nomic.project:create_index:1192 - Created map `test_index_1677255228.136989_index` in project `test_index_1677255228.136989`: https://atlas.nomic.ai/map/ee2354a3-7f9a-4c6b-af43-b0cda09d7198/db996d77-8981-48a0-897a-ff2c22bbf541\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = AtlasDB.from_texts(texts=texts,\n",
+    "                        name='test_index_'+str(time.time()),\n",
+    "                        description='test_index',\n",
+    "                        api_key=ATLAS_TEST_API_KEY,\n",
+    "                        index_kwargs={'build_topic_model': True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-24 16:14:09.106 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with db.project.wait_for_project_lock():\n",
+    "    time.sleep(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <strong><a href=\"https://atlas.nomic.ai/dashboard/project/ee2354a3-7f9a-4c6b-af43-b0cda09d7198\">test_index_1677255228.136989</strong></a>\n",
+       "            <br>\n",
+       "            A description for your project 508 datums inserted.\n",
+       "            <br>\n",
+       "            1 index built.\n",
+       "            <br><strong>Projections</strong>\n",
+       "<ul>\n",
+       "<li>test_index_1677255228.136989_index. Status Completed. <a target=\"_blank\" href=\"https://atlas.nomic.ai/map/ee2354a3-7f9a-4c6b-af43-b0cda09d7198/db996d77-8981-48a0-897a-ff2c22bbf541\">view online</a></li></ul><hr><script>\n",
+       "            destroy = function() {\n",
+       "                document.getElementById(\"iframedb996d77-8981-48a0-897a-ff2c22bbf541\").remove()\n",
+       "            }\n",
+       "        </script>\n",
+       "\n",
+       "        <h4>Projection ID: db996d77-8981-48a0-897a-ff2c22bbf541</h4>\n",
+       "        <div class=\"actions\">\n",
+       "            <div id=\"hide\" class=\"action\" onclick=\"destroy()\">Hide embedded project</div>\n",
+       "            <div class=\"action\" id=\"out\">\n",
+       "                <a href=\"https://atlas.nomic.ai/map/ee2354a3-7f9a-4c6b-af43-b0cda09d7198/db996d77-8981-48a0-897a-ff2c22bbf541\" target=\"_blank\">Explore on atlas.nomic.ai</a>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <iframe class=\"iframe\" id=\"iframedb996d77-8981-48a0-897a-ff2c22bbf541\" allow=\"clipboard-read; clipboard-write\" src=\"https://atlas.nomic.ai/map/ee2354a3-7f9a-4c6b-af43-b0cda09d7198/db996d77-8981-48a0-897a-ff2c22bbf541\">\n",
+       "        </iframe>\n",
+       "\n",
+       "        <style>\n",
+       "            .iframe {\n",
+       "                /* vh can be **very** large in vscode ipynb. */\n",
+       "                height: min(75vh, 66vw);\n",
+       "                width: 100%;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <style>\n",
+       "            .actions {\n",
+       "              display: block;\n",
+       "            }\n",
+       "            .action {\n",
+       "              min-height: 18px;\n",
+       "              margin: 5px;\n",
+       "              transition: all 500ms ease-in-out;\n",
+       "            }\n",
+       "            .action:hover {\n",
+       "              cursor: pointer;\n",
+       "            }\n",
+       "            #hide:hover::after {\n",
+       "                content: \" X\";\n",
+       "            }\n",
+       "            #out:hover::after {\n",
+       "                content: \"\";\n",
+       "            }\n",
+       "        </style>\n",
+       "        "
+      ],
+      "text/plain": [
+       "AtlasProject: <{'id': 'ee2354a3-7f9a-4c6b-af43-b0cda09d7198', 'owner': '9c29afbb-a002-4d49-958e-ecf5ae1351ac', 'project_name': 'test_index_1677255228.136989', 'creator': 'auth0|63efc4b5462246f4d9a6ecf2', 'description': 'A description for your project', 'opensearch_index_id': 'f61fb8dd-0abf-4f31-9130-41870e443902', 'is_public': True, 'project_fields': ['atlas_id', 'text'], 'unique_id_field': 'atlas_id', 'modality': 'text', 'total_datums_in_project': 508, 'created_timestamp': '2023-02-24T16:13:50.313363+00:00', 'atlas_indices': [{'id': 'b1b01833-0964-4597-a4bc-a2d60700949d', 'project_id': 'ee2354a3-7f9a-4c6b-af43-b0cda09d7198', 'index_name': 'test_index_1677255228.136989_index', 'indexed_field': 'text', 'created_timestamp': '2023-02-24T16:13:52.957101+00:00', 'updated_timestamp': '2023-02-24T16:14:03.469621+00:00', 'atoms': ['charchunk', 'document'], 'colorable_fields': [], 'embedders': [{'id': '7ec0868a-4eed-4414-a482-25cce9803e1b', 'atlas_index_id': 'b1b01833-0964-4597-a4bc-a2d60700949d', 'ready': True, 'model_name': 'NomicEmbed', 'hyperparameters': {'norm': 'both', 'batch_size': 20, 'polymerize_by': 'charchunk', 'dataset_buffer_size': 1000}}], 'nearest_neighbor_indices': [{'id': '86f8e3ff-e07c-4678-a4d7-144db4b0301d', 'index_name': 'NomicOrganize', 'ready': True, 'hyperparameters': {'dim': 384, 'space': 'l2'}, 'atom_strategies': ['document']}], 'projections': [{'id': 'db996d77-8981-48a0-897a-ff2c22bbf541', 'projection_name': 'NomicProject', 'ready': True, 'hyperparameters': {'spread': 1.0, 'n_epochs': 50, 'n_neighbors': 15}, 'atom_strategies': ['document'], 'created_timestamp': '2023-02-24T16:13:52.979561+00:00', 'updated_timestamp': '2023-02-24T16:14:03.466309+00:00'}]}], 'insert_update_delete_lock': False}>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.project"
+   ]
+  },
+  {
+   "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": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/modules/indexes/vectorstore_examples/deeplake.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Deep Lake\n",
+    "\n",
+    "This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks.  \n",
+    "\n",
+    "For more information, please see the Deep Lake [documentation](docs.activeloop.ai) or [api reference](docs.deeplake.ai)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DeepLake\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating ingest: 100%|██████████| 41/41 [00:00<00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = DeepLake.from_documents(docs, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
+      "\n",
+      "We cannot let this happen. \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": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deep Lake datasets on cloud or local\n",
+    "By default deep lake datasets are stored in memory, in case you want to persist locally or to any object storage you can simply provide path to the dataset. You can retrieve token from [app.activeloop.ai](https://app.activeloop.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/bin/bash: -c: line 0: syntax error near unexpected token `newline'\n",
+      "/bin/bash: -c: line 0: `activeloop login -t <token>'\n"
+     ]
+    }
+   ],
+   "source": [
+    "!activeloop login -t <token>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating ingest: 100%|██████████| 4/4 [00:00<00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Embed and store the texts\n",
+    "dataset_path = \"hub://{username}/{dataset_name}\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://, etc.\n",
+    "\n",
+    "embedding = OpenAIEmbeddings()\n",
+    "vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
+      "\n",
+      "We cannot let this happen. \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": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)\n",
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset(path='./local/path', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "\n",
+      "  tensor     htype     shape     dtype  compression\n",
+      "  -------   -------   -------   -------  ------- \n",
+      " embedding  generic  (4, 1536)   None     None   \n",
+      "    ids      text     (4, 1)      str     None   \n",
+      " metadata    json     (4, 1)      str     None   \n",
+      "   text      text     (4, 1)      str     None   \n"
+     ]
+    }
+   ],
+   "source": [
+    "vectordb.ds.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = vectordb.ds.embedding.numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7b14174bb6f9d4680b62ac2a6390e1ce94fbfabf172a10844870451d539c58d6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/llms/async_llm.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f6574496-b360-4ffa-9523-7fd34a590164",
    "metadata": {},
@@ -10,14 +9,14 @@
     "\n",
     "LangChain provides async support for LLMs by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",
-    "Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, only `OpenAI` and `PromptLayerOpenAI` is supported, but async support for other LLMs is on the roadmap.\n",
+    "Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, only `OpenAI` `OpenAIChat`, and `PromptLayerOpenAI` are supported, but async support for other LLMs is on the roadmap.\n",
     "\n",
     "You can use the `agenerate` method to call an OpenAI LLM asynchronously."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "5e49e96c-0f88-466d-b3d3-ea0966bdf19e",
    "metadata": {
     "tags": []
@@ -29,64 +28,66 @@
      "text": [
       "\n",
       "\n",
-      "I'm doing well. How about you?\n",
+      "As an AI language model, I don't have feelings like humans, but I'm functioning properly. How may I assist you?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "I'm an AI language model, so I don't have emotions, but I'm functioning properly. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have emotions like humans, but I'm functioning normally. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
-      "\n",
-      "I am doing quite well. How about you?\n",
+      "I am an AI language model, so I do not have feelings, but I am here to assist you. How may I help you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have feelings or emotions but I'm always ready to assist you. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing great, thank you! How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning normally. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thanks for asking. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning properly. Thank you. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have emotions, so I don't have a specific feeling or emotion. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
-      "\u001b[1mConcurrent executed in 1.93 seconds.\u001b[0m\n",
+      "As an AI language model, I do not have feelings or emotions. However, I am functioning as intended and ready to assist you with any queries you may have. How can I be of assistance today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have feelings, but I am functioning well. Thank you for asking. How can I assist you today?\n",
+      "\u001b[1mConcurrent executed in 0.92 seconds.\u001b[0m\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. Thank you for asking. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "I'm an AI language model, so I don't have feelings, but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. Thank you for asking. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
-      "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I am functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have personal emotions. However, I am functioning well and ready to assist you with any queries or tasks you have. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing great, thank you. How about you?\n",
-      "\u001b[1mSerial executed in 10.54 seconds.\u001b[0m\n"
+      "As an AI language model, I do not have feelings or emotions, but I'm functioning well. How can I assist you today?\n",
+      "\n",
+      "\n",
+      "I am an AI language model and do not have feelings. But I am functioning properly and ready to assist you with any task. How may I help you today?\n",
+      "\n",
+      "\n",
+      "As an AI language model, I do not have emotions, but I am functioning well. How can I assist you today?\n",
+      "\u001b[1mSerial executed in 5.00 seconds.\u001b[0m\n"
      ]
     }
    ],
@@ -94,10 +95,10 @@
     "import time\n",
     "import asyncio\n",
     "\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain.llms import OpenAIChat\n",
     "\n",
     "def generate_serially():\n",
-    "    llm = OpenAI(temperature=0.9)\n",
+    "    llm = OpenAIChat(temperature=0.9)\n",
     "    for _ in range(10):\n",
     "        resp = llm.generate([\"Hello, how are you?\"])\n",
     "        print(resp.generations[0][0].text)\n",
@@ -109,7 +110,7 @@
     "\n",
     "\n",
     "async def generate_concurrently():\n",
-    "    llm = OpenAI(temperature=0.9)\n",
+    "    llm = OpenAIChat(temperature=0.9)\n",
     "    tasks = [async_generate(llm) for _ in range(10)]\n",
     "    await asyncio.gather(*tasks)\n",
     "\n",
@@ -125,6 +126,14 @@
     "elapsed = time.perf_counter() - s\n",
     "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1d3a966-3a27-44e8-9441-ed72f01b86f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/modules/llms/examples/fake_llm.ipynb

@@ -80,14 +80,14 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction: Python REPL\n",
-      "Action Input: print(2 + 2)\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m4\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mFinal Answer: 4\u001b[0m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mAction: Python REPL\n",
+      "Action Input: print(2 + 2)\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3m4\n",
+      "\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3mFinal Answer: 4\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/llms/examples/llm_serialization.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# LLM Serialization\n",
     "\n",
-    "This notebook walks how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (eg the provider, the temperature, etc)."
+    "This notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc)."
    ]
   },
   {

docs/modules/llms/integrations.rst

@@ -31,13 +31,13 @@ The examples here are all "how-to" guides for how to integrate with various LLM
 
 `Forefront AI <./integrations/forefrontai_example.html>`_: Covers how to utilize the Forefront AI wrapper.
 
-`PromptLayer OpenAI <./integrations/promptlayer_openai.html>`_: Covers how to use `PromptLayer <https://promptlayer.com>`_ with Langchain.
+`PromptLayer OpenAI <./integrations/promptlayer_openai.html>`_: Covers how to use `PromptLayer <https://promptlayer.com>`_ with LangChain.
 
-`Anthropic <./integrations/anthropic_example.html>`_: Covers how to use Anthropic models with Langchain.
+`Anthropic <./integrations/anthropic_example.html>`_: Covers how to use Anthropic models with LangChain.
 
 `DeepInfra <./integrations/deepinfra_example.html>`_: Covers how to utilize the DeepInfra wrapper.
 
-`Self-Hosted Models (via Runhouse) <./integrations/self_hosted_examples.html>`_: Covers how to run models on existing or on-demand remote compute with Langchain.
+`Self-Hosted Models (via Runhouse) <./integrations/self_hosted_examples.html>`_: Covers how to run models on existing or on-demand remote compute with LangChain.
 
 
 .. toctree::

docs/modules/llms/integrations/openai.ipynb

@@ -76,19 +76,11 @@
     "\n",
     "llm_chain.run(question)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4797d719",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('palm')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -102,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/llms/integrations/openaichat.ipynb

@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# OpenAIChat\n",
+    "\n",
+    "OpenAI also has a [chat model](https://platform.openai.com/docs/guides/chat) you can use. The interface is very similar to the normal OpenAI model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "522686de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAIChat\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "62e0dbc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAIChat(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fbb043e6",
+   "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,
+   "id": "3f945b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "25260808",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nJustin Bieber was born on March 1, 1994. \\n\\nThe Super Bowl is played in February of each year. \\n\\nTherefore, the Super Bowl that was played in the year Justin Bieber was born was Super Bowl XXVIII, which was played on January 30, 1994. \\n\\nThe Dallas Cowboys won Super Bowl XXVIII by defeating the Buffalo Bills with a score of 30-13.'"
+      ]
+     },
+     "execution_count": 5,
+     "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": "markdown",
+   "id": "75a05b79",
+   "metadata": {},
+   "source": [
+    "## Prefix Messages\n",
+    "\n",
+    "OpenAI Chat also supports the idea of [prefix messages](https://platform.openai.com/docs/guides/chat/chat-vs-completions), eg messages that would appear before the user input. These can be used as system messages to give more context/purpose the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c27a1501",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix_messages = [{\"role\": \"system\", \"content\": \"You are a helpful assistant that is very good at problem solving who thinks step by step.\"}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e46a914e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAIChat(temperature=0, prefix_messages=prefix_messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d683d9f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f5b8e78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Step 1: Justin Bieber was born on March 1, 1994.\\nStep 2: The Super Bowl is played in February of each year.\\nStep 3: Therefore, the Super Bowl that was played in the year Justin Bieber was born was Super Bowl XXVIII, which was played on January 30, 1994.\\nStep 4: The team that won Super Bowl XXVIII was the Dallas Cowboys.'"
+      ]
+     },
+     "execution_count": 9,
+     "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": "markdown",
+   "id": "f6d5dda8",
+   "metadata": {},
+   "source": [
+    "## Async"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1973b9bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = await llm_chain.arun(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5815178f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Step 1: Justin Bieber was born on March 1, 1994.\\nStep 2: The Super Bowl is played in February of each year.\\nStep 3: Therefore, the Super Bowl that was played in the year Justin Bieber was born was Super Bowl XXVIII, which was played on January 30, 1994.\\nStep 4: The team that won Super Bowl XXVIII was the Dallas Cowboys.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb779f3f",
+   "metadata": {},
+   "source": [
+    "## Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "509181be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Justin Bieber was born on March 1, 1994. The NFL team that won the Super Bowl in the same year was the Dallas Cowboys. They defeated the Buffalo Bills 30-13 in Super Bowl XXVIII on January 30, 1994."
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from langchain.callbacks.base import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "llm = OpenAIChat(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "resp = llm(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c095285d",
+   "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/llms/key_concepts.md

@@ -2,9 +2,9 @@
 
 ## LLMs
 Wrappers around Large Language Models (in particular, the "generate" ability of large language models) are at the core of LangChain functionality.
-The core method that these classes expose is a `generate` method, which takes in a list of strings and returns an LLMResult (which contains outputs for all input strings).
-Read more about LLMResult. This interface operates over a list of strings because often the lists of strings can be batched to the LLM provider,
-providing speed and efficiency gains.
+The core method that these classes expose is a `generate` method, which takes in a list of strings and returns an LLMResult (which contains outputs for all input strings). Read more about [LLMResult](#llmresult). 
+
+This interface operates over a list of strings because often the lists of strings can be batched to the LLM provider, providing speed and efficiency gains.
 For convenience, this class also exposes a simpler, more user friendly interface (via `__call__`). 
 The interface for this takes in a single string, and returns a single string.
 

docs/modules/llms/streaming_llm.ipynb

@@ -7,12 +7,12 @@
    "source": [
     "# Streaming with LLMs\n",
     "\n",
-    "LangChain provides streaming support for LLMs. Currently, we only support streaming for the `OpenAI` LLM implementation, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
+    "LangChain provides streaming support for LLMs. Currently, we only support streaming for the `OpenAI` and `OpenAIChat` LLM implementation, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "4ac0ff54-540a-4f2b-8d9a-b590fec7fe07",
    "metadata": {
     "tags": []
@@ -27,43 +27,43 @@
       "Verse 1\n",
       "I'm sippin' on sparkling water,\n",
       "It's so refreshing and light,\n",
-      "It's the perfect way to quench my thirst,\n",
+      "It's the perfect way to quench my thirst\n",
       "On a hot summer night.\n",
       "\n",
       "Chorus\n",
       "Sparkling water, sparkling water,\n",
       "It's the best way to stay hydrated,\n",
-      "It's so refreshing and light,\n",
-      "It's the perfect way to stay alive.\n",
+      "It's so crisp and so clean,\n",
+      "It's the perfect way to stay refreshed.\n",
       "\n",
       "Verse 2\n",
       "I'm sippin' on sparkling water,\n",
       "It's so bubbly and bright,\n",
-      "It's the perfect way to cool me down,\n",
+      "It's the perfect way to cool me down\n",
       "On a hot summer night.\n",
       "\n",
       "Chorus\n",
       "Sparkling water, sparkling water,\n",
       "It's the best way to stay hydrated,\n",
-      "It's so refreshing and light,\n",
-      "It's the perfect way to stay alive.\n",
+      "It's so crisp and so clean,\n",
+      "It's the perfect way to stay refreshed.\n",
       "\n",
       "Verse 3\n",
       "I'm sippin' on sparkling water,\n",
-      "It's so crisp and clean,\n",
-      "It's the perfect way to keep me going,\n",
-      "On a hot summer day.\n",
+      "It's so light and so clear,\n",
+      "It's the perfect way to keep me cool\n",
+      "On a hot summer night.\n",
       "\n",
       "Chorus\n",
       "Sparkling water, sparkling water,\n",
       "It's the best way to stay hydrated,\n",
-      "It's so refreshing and light,\n",
-      "It's the perfect way to stay alive."
+      "It's so crisp and so clean,\n",
+      "It's the perfect way to stay refreshed."
      ]
     }
    ],
    "source": [
-    "from langchain.llms import OpenAI\n",
+    "from langchain.llms import OpenAI, OpenAIChat\n",
     "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "\n",
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "a35373f1-9ee6-4753-a343-5aee749b8527",
    "metadata": {
     "tags": []
@@ -103,10 +103,10 @@
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {}})"
+       "LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', generation_info={'finish_reason': None, 'logprobs': None})]], llm_output={'token_usage': {}})"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -114,6 +114,85 @@
    "source": [
     "llm.generate([\"Tell me a joke.\"])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a93a4d61-0476-49db-8321-7de92bd74059",
+   "metadata": {},
+   "source": [
+    "Here's an example with `OpenAIChat`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "22665f16-e05b-473c-a4bd-ad75744ea024",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Verse 1:\n",
+      "Bubbles rising to the top\n",
+      "A refreshing drink that never stops\n",
+      "Clear and crisp, it's pure delight\n",
+      "A taste that's sure to excite\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Verse 2:\n",
+      "No sugar, no calories, just pure bliss\n",
+      "A drink that's hard to resist\n",
+      "It's the perfect way to quench my thirst\n",
+      "A drink that always comes first\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Bridge:\n",
+      "From the mountains to the sea\n",
+      "Sparkling water, you're the key\n",
+      "To a healthy life, a happy soul\n",
+      "A drink that makes me feel whole\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Outro:\n",
+      "Sparkling water, you're the one\n",
+      "A drink that's always so much fun\n",
+      "I'll never let you go, my friend\n",
+      "Sparkling water, until the end."
+     ]
+    }
+   ],
+   "source": [
+    "llm = OpenAIChat(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "resp = llm(\"Write me a song about sparkling water.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eadae4ba-9f21-4ec8-845d-dd43b0edc2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/modules/memory/examples/chatgpt_clone.ipynb

@@ -24,9 +24,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -36,9 +36,9 @@
       "\n",
       "\n",
       "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "```\n",
       "$ pwd\n",
@@ -93,9 +93,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -110,9 +110,9 @@
       "/\n",
       "```\n",
       "Human: ls ~\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "```\n",
       "$ ls ~\n",
@@ -138,9 +138,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -161,9 +161,9 @@
       "Desktop  Documents  Downloads  Music  Pictures  Public  Templates  Videos\n",
       "```\n",
       "Human: cd ~\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "```\n",
       "$ cd ~\n",
@@ -190,9 +190,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -214,9 +214,9 @@
       "/home/user\n",
       "```\n",
       "Human: {Please make a file jokes.txt inside and put some jokes inside}\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -245,9 +245,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -272,9 +272,9 @@
       "$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
       "```\n",
       "Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -304,9 +304,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -332,9 +332,9 @@
       "Result: 33\n",
       "```\n",
       "Human: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -362,9 +362,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -391,9 +391,9 @@
       "Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
       "COPY entrypoint.sh entrypoint.sh\n",
       "ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -426,9 +426,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -459,9 +459,9 @@
       "Hello from Docker\n",
       "```\n",
       "Human: nvidia-smi\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -502,9 +502,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -548,9 +548,9 @@
       "|=============================================================================|\n",
       "\n",
       "Human: ping bbc.com\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -584,9 +584,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -630,9 +630,9 @@
       "round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms\n",
       "```\n",
       "Human: curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -659,9 +659,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -691,9 +691,9 @@
       "1.8.1\n",
       "```\n",
       "Human: lynx https://www.deepmind.com/careers\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -726,9 +726,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -757,9 +757,9 @@
       "Explore our current openings and apply today. We look forward to hearing from you.\n",
       "```\n",
       "Human: curl https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "\n",
       "```\n",
@@ -799,9 +799,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -843,9 +843,9 @@
       "</html>\n",
       "```\n",
       "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -875,9 +875,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -916,9 +916,9 @@
       "}\n",
       "```\n",
       "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "\n",
       "```\n",

docs/modules/memory/examples/conversational_agent.ipynb

@@ -76,12 +76,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
+      "AI: Hi Bob, nice to meet you! How can I help you today?\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -111,12 +111,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: Your name is Bob!\u001b[0m\n",
+      "AI: Your name is Bob!\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -146,12 +146,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001b[0m\n",
+      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -181,16 +181,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
-      "Action Input: Who won the World Cup in 1978\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001b[0m\n",
+      "Action Input: Who won the World Cup in 1978\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m Do I need to use a tool? No\n",
+      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -220,16 +220,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
-      "Action Input: Current temperature in Pomfret\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001b[0m\n",
+      "Action Input: Current temperature in Pomfret\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m Do I need to use a tool? No\n",
+      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/memory/getting_started.ipynb

@@ -51,16 +51,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "Human: Hi there!\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -90,18 +90,18 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "Human: Hi there!\n",
       "AI:  Hi there! It's nice to meet you. How can I help you today?\n",
       "Human: I'm doing well! Just having a conversation with an AI.\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -131,9 +131,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
@@ -142,9 +142,9 @@
       "Human: I'm doing well! Just having a conversation with an AI.\n",
       "AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\n",
       "Human: Tell me about yourself.\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -195,16 +195,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -239,17 +239,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "The human greets the AI and the AI responds, saying it is doing well and is currently helping a customer with a technical issue.\n",
       "Human: Tell me more about it!\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -279,18 +279,18 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "\n",
       "The human greets the AI and the AI responds, saying it is doing well and is currently helping a customer with a technical issue. The customer is having trouble with their computer not connecting to the internet, and the AI is helping them troubleshoot the issue by resetting the router and checking the network settings. They are still looking into other possible causes.\n",
       "Human: Very cool -- what is the scope of the project?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -342,16 +342,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -387,17 +387,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "Human: Hi, what's up?\n",
       "AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
       "Human: What's their issues?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -427,9 +427,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "Human: Hi, what's up?\n",
@@ -437,9 +437,9 @@
       "Human: What's their issues?\n",
       "AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\n",
       "Human: Is it going well?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -469,9 +469,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "Human: What's their issues?\n",
@@ -479,9 +479,9 @@
       "Human: Is it going well?\n",
       "AI:  Yes, it's going well so far. We've already identified the problem and are now working on a solution.\n",
       "Human: What's the solution?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -534,16 +534,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -579,17 +579,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "Human: Hi, what's up?\n",
       "AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
       "Human: Just working on writing some documentation!\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -619,9 +619,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
@@ -629,9 +629,9 @@
       "Human: Just working on writing some documentation!\n",
       "AI:  That sounds like a lot of work. What kind of documentation are you writing?\n",
       "Human: For LangChain! Have you heard of it?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -662,18 +662,18 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
       "\n",
       "Current conversation:\n",
       "\n",
       "The human asked the AI what it was up to, and the AI responded that it was helping a customer with a technical issue. The human then mentioned they were writing documentation for LangChain, a blockchain-based language learning platform, and the AI revealed they had heard of it and asked the human to tell them more about the documentation they were writing.\n",
       "\n",
       "Human: Haha nope, although a lot of people confuse it for that\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished ConversationChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -755,9 +755,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
       "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
       "\n",
       "Relevant Information:\n",
@@ -766,9 +766,9 @@
       "\n",
       "Conversation:\n",
       "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -798,9 +798,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
       "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
       "\n",
       "Relevant Information:\n",
@@ -809,9 +809,9 @@
       "\n",
       "Conversation:\n",
       "Human: My name is James and I'm helping Will. He's an engineer.\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -841,9 +841,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new ConversationChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
+      "\u001B[32;1m\u001B[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
       "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
       "\n",
       "Relevant Information:\n",
@@ -852,9 +852,9 @@
       "\n",
       "Conversation:\n",
       "Human: What do you know about Will?\n",
-      "AI:\u001b[0m\n",
+      "AI:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/modules/prompts/examples/custom_example_selector.md

@@ -1,6 +1,6 @@
 # Create a custom example selector
 
-In this tutorial, we'll create a custom example selector that selects examples every alternate example given a list of examples.
+In this tutorial, we'll create a custom example selector that selects every alternate example from a given list of examples.
 
 An `ExampleSelector` must implement two methods:
 
@@ -65,4 +65,4 @@ example_selector.examples
 # Select examples
 example_selector.select_examples({"foo": "foo"})
 # -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)
-```
+```

docs/modules/prompts/examples/example_prompt.json

@@ -1,4 +1,5 @@
 {
+    "_type": "prompt",
     "input_variables": ["input", "output"],
     "template": "Input: {input}\nOutput: {output}" 
 }

docs/modules/prompts/examples/few_shot_prompt.json

@@ -3,6 +3,7 @@
     "input_variables": ["adjective"],
     "prefix": "Write antonyms for the following words.",
     "example_prompt": {
+        "_type": "prompt",
         "input_variables": ["input", "output"],
         "template": "Input: {input}\nOutput: {output}"
     },

docs/modules/prompts/examples/few_shot_prompt.yaml

@@ -4,6 +4,7 @@ input_variables:
 prefix: 
     Write antonyms for the following words.
 example_prompt:
+    _type: prompt
     input_variables:
         ["input", "output"]
     template:

docs/modules/prompts/examples/few_shot_prompt_examples_in.json

@@ -3,6 +3,7 @@
     "input_variables": ["adjective"],
     "prefix": "Write antonyms for the following words.",
     "example_prompt": {
+        "_type": "prompt",
         "input_variables": ["input", "output"],
         "template": "Input: {input}\nOutput: {output}"
     },

docs/modules/prompts/examples/few_shot_prompt_yaml_examples.yaml

@@ -4,6 +4,7 @@ input_variables:
 prefix: 
     Write antonyms for the following words.
 example_prompt:
+    _type: prompt
     input_variables:
         ["input", "output"]
     template:

docs/modules/prompts/examples/partial.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9355a547",
+   "metadata": {},
+   "source": [
+    "# Partial Prompt Templates\n",
+    "\n",
+    "A prompt template is a class with a `.format` method which takes in a key-value map and returns a string (a prompt) to pass to the language model. Like other methods, it can make sense to \"partial\" a prompt template - eg pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values.\n",
+    "\n",
+    "LangChain supports this in two ways: we allow for partially formatted prompts (1) with string values, (2) with functions that return string values. These two different ways support different use cases. In the documentation below we go over the motivations for both use cases as well as how to do it in LangChain.\n",
+    "\n",
+    "## Partial With Strings\n",
+    "\n",
+    "One common use case for wanting to partial a prompt template is if you get some of the variables before others. For example, suppose you have a prompt template that requires two variables, `foo` and `baz`. If you get the `foo` value early on in the chain, but the `baz` value later, it can be annoying to wait until you have both variables in the same place to pass them to the prompt template. Instead, you can partial the prompt template with the `foo` value, and then pass the partialed prompt template along and just use that. Below is an example of doing this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "643af5da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4080d8d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "foobaz\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = PromptTemplate(template=\"{foo}{bar}\", input_variables=[\"foo\", \"bar\"])\n",
+    "partial_prompt = prompt.partial(foo=\"foo\");\n",
+    "print(partial_prompt.format(bar=\"baz\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9986766e",
+   "metadata": {},
+   "source": [
+    "You can also just initialize the prompt with the partialed variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2ce95b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "foobaz\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = PromptTemplate(template=\"{foo}{bar}\", input_variables=[\"bar\"], partial_variables={\"foo\": \"foo\"})\n",
+    "print(prompt.format(bar=\"baz\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9c66f83",
+   "metadata": {},
+   "source": [
+    "## Partial With Functions\n",
+    "\n",
+    "The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to have the current date. You can't hard code it in the prompt, and passing it along with the other input variables is a bit annoying. In this case, it's very handy to be able to partial the prompt with a function that always returns the current date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d0712d8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "def _get_datetime():\n",
+    "    now = datetime.now()\n",
+    "    return now.strftime(\"%m/%d/%Y, %H:%M:%S\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4cbcb666",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me a funny joke about the day 02/27/2023, 22:15:16\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = PromptTemplate(\n",
+    "    template=\"Tell me a {adjective} joke about the day {date}\", \n",
+    "    input_variables=[\"adjective\", \"date\"]\n",
+    ");\n",
+    "partial_prompt = prompt.partial(date=_get_datetime)\n",
+    "print(partial_prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffed6811",
+   "metadata": {},
+   "source": [
+    "You can also just initialize the prompt with the partialed variables, which often makes more sense in this workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "96285b25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me a funny joke about the day 02/27/2023, 22:15:16\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = PromptTemplate(\n",
+    "    template=\"Tell me a {adjective} joke about the day {date}\", \n",
+    "    input_variables=[\"adjective\"],\n",
+    "    partial_variables={\"date\": _get_datetime}\n",
+    ");\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bff16f7",
+   "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/prompts/examples/prompt_serialization.ipynb

@@ -58,6 +58,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "_type: prompt\r\n",
       "input_variables:\r\n",
       "    [\"adjective\", \"content\"]\r\n",
       "template: \r\n",
@@ -108,6 +109,7 @@
      "output_type": "stream",
      "text": [
       "{\r\n",
+      "    \"_type\": \"prompt\",\r\n",
       "    \"input_variables\": [\"adjective\", \"content\"],\r\n",
       "    \"template\": \"Tell me a {adjective} joke about {content}.\"\r\n",
       "}\r\n"
@@ -156,6 +158,7 @@
      "output_type": "stream",
      "text": [
       "{\r\n",
+      "    \"_type\": \"prompt\",\r\n",
       "    \"input_variables\": [\"adjective\", \"content\"],\r\n",
       "    \"template_path\": \"simple_template.txt\"\r\n",
       "}\r\n"
@@ -279,6 +282,7 @@
       "prefix: \r\n",
       "    Write antonyms for the following words.\r\n",
       "example_prompt:\r\n",
+      "    _type: prompt\r\n",
       "    input_variables:\r\n",
       "        [\"input\", \"output\"]\r\n",
       "    template:\r\n",
@@ -346,6 +350,7 @@
       "prefix: \r\n",
       "    Write antonyms for the following words.\r\n",
       "example_prompt:\r\n",
+      "    _type: prompt\r\n",
       "    input_variables:\r\n",
       "        [\"input\", \"output\"]\r\n",
       "    template:\r\n",
@@ -413,6 +418,7 @@
       "    \"input_variables\": [\"adjective\"],\r\n",
       "    \"prefix\": \"Write antonyms for the following words.\",\r\n",
       "    \"example_prompt\": {\r\n",
+      "        \"_type\": \"prompt\",\r\n",
       "        \"input_variables\": [\"input\", \"output\"],\r\n",
       "        \"template\": \"Input: {input}\\nOutput: {output}\"\r\n",
       "    },\r\n",
@@ -478,6 +484,7 @@
       "    \"input_variables\": [\"adjective\"],\r\n",
       "    \"prefix\": \"Write antonyms for the following words.\",\r\n",
       "    \"example_prompt\": {\r\n",
+      "        \"_type\": \"prompt\",\r\n",
       "        \"input_variables\": [\"input\", \"output\"],\r\n",
       "        \"template\": \"Input: {input}\\nOutput: {output}\"\r\n",
       "    },\r\n",
@@ -542,6 +549,7 @@
      "output_type": "stream",
      "text": [
       "{\r\n",
+      "    \"_type\": \"prompt\",\r\n",
       "    \"input_variables\": [\"input\", \"output\"],\r\n",
       "    \"template\": \"Input: {input}\\nOutput: {output}\" \r\n",
       "}\r\n"
@@ -622,7 +630,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.2"
   },
   "vscode": {
    "interpreter": {

docs/modules/prompts/examples/simple_prompt.json

@@ -1,4 +1,5 @@
 {
+    "_type": "prompt",
     "input_variables": ["adjective", "content"],
     "template": "Tell me a {adjective} joke about {content}."
 }

docs/modules/prompts/examples/simple_prompt.yaml

@@ -1,3 +1,4 @@
+_type: prompt
 input_variables:
     ["adjective", "content"]
 template: 

docs/modules/prompts/examples/simple_prompt_with_template_file.json

@@ -1,4 +1,5 @@
 {
+    "_type": "prompt",
     "input_variables": ["adjective", "content"],
     "template_path": "simple_template.txt"
 }

docs/modules/prompts/getting_started.md

@@ -8,7 +8,7 @@ In this tutorial, we will learn about:
 
 ## What is a prompt template?
 
-A prompt template refers to a reproducible way to generate a prompt. It contains a text string ("the template"), that can can take in a set of parameters from the end user and generate a prompt.
+A prompt template refers to a reproducible way to generate a prompt. It contains a text string ("the template"), that can take in a set of parameters from the end user and generate a prompt.
 
 The prompt template may contain:
 - instructions to the language model,

docs/modules/prompts/how_to_guides.rst

@@ -17,6 +17,8 @@ The user guide here shows more advanced workflows and how to use the library in
 
 `Few Shot Prompt Examples <./examples/few_shot_examples.html>`_: Examples of Few Shot Prompt Templates.
 
+`Partial Prompt Template <./examples/partial.html>`_: How to partial Prompt Templates.
+
 
 
 .. toctree::

docs/modules/prompts/key_concepts.md

@@ -62,7 +62,7 @@ To learn more about how to provide few shot examples, see [Few Shot Examples](ex
 
 If there are multiple examples that are relevant to a prompt, it is important to select the most relevant examples. Generally, the quality of the response from the LLM can be significantly improved by selecting the most relevant examples. This is because the language model will be able to better understand the context of the prompt, and also potentially learn failure modes to avoid.
 
-To help the user with selecting the most relevant examples, we provide example selectors that select the most relevant based on different criteria, such as length, semantic similarity, etc. The example selector takes in a list of examples and returns a list of selected examples, formatted as a string. The user can also provide their own example selector. To learn more about example selectors, see [Example Selection](example_selection.md).
+To help the user with selecting the most relevant examples, we provide example selectors that select the most relevant based on different criteria, such as length, semantic similarity, etc. The example selector takes in a list of examples and returns a list of selected examples, formatted as a string. The user can also provide their own example selector. To learn more about example selectors, see [Example Selectors](examples/example_selectors.ipynb).
 
 <!-- TODO(shreya): Add correct link here. -->
 

docs/modules/utils/examples/bash.ipynb

@@ -1,85 +1,85 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8f210ec3",
-   "metadata": {},
-   "source": [
-    "# Bash\n",
-    "It can often be useful to have an LLM generate bash commands, and then run them. A common use case this is for letting it interact with your local file system. We provide an easy util to execute bash commands."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f7b3767b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import BashProcess"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cf1c92f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bash = BashProcess()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2fa952fc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bash.ipynb\n",
-      "google_search.ipynb\n",
-      "python.ipynb\n",
-      "requests.ipynb\n",
-      "serpapi.ipynb\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "id": "8f210ec3",
+      "metadata": {},
+      "source": [
+        "# Bash\n",
+        "It can often be useful to have an LLM generate bash commands, and then run them. A common use case for this is letting the LLM interact with your local file system. We provide an easy util to execute bash commands."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "f7b3767b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain.utilities import BashProcess"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cf1c92f0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "bash = BashProcess()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "2fa952fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "bash.ipynb\n",
+            "google_search.ipynb\n",
+            "python.ipynb\n",
+            "requests.ipynb\n",
+            "serpapi.ipynb\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(bash.run(\"ls\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "851fee9f",
+      "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"
     }
-   ],
-   "source": [
-    "print(bash.run(\"ls\"))"
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "851fee9f",
-   "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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/modules/utils/key_concepts.md

@@ -1,29 +1,35 @@
 # Key Concepts
 
 ## Python REPL
-Sometimes, for complex calculations, rather than have an LLM generate the answer directly, 
-it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. 
+
+Sometimes, for complex calculations, rather than have an LLM generate the answer directly,
+it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer.
 In order to easily do that, we provide a simple Python REPL to execute commands in.
-This interface will only return things that are printed - 
+This interface will only return things that are printed -
 therefore, if you want to use it to calculate an answer, make sure to have it print out the answer.
 
 ## Bash
-It can often be useful to have an LLM generate bash commands, and then run them. 
-A common use case this is for letting it interact with your local file system. 
+
+It can often be useful to have an LLM generate bash commands, and then run them.
+A common use case for this is letting the LLM interact with your local file system.
 We provide an easy component to execute bash commands.
 
 ## Requests Wrapper
-The web contains a lot of information that LLMs do not have access to. 
-In order to easily let LLMs interact with that information, 
+
+The web contains a lot of information that LLMs do not have access to.
+In order to easily let LLMs interact with that information,
 we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
 
 ## Google Search
+
 This uses the official Google Search API to look up information on the web.
 
 ## SerpAPI
+
 This uses SerpAPI, a third party search API engine, to interact with Google Search.
 
 ## Searx Search
+
 This uses the Searx (SearxNG fork) meta search engine API to lookup information
-on the web.  It supports 139 search engines and is easy to self-host
+on the web. It supports 139 search engines and is easy to self-host
 which makes it a good choice for privacy-conscious users.

docs/reference/integrations.md

@@ -50,6 +50,8 @@ The following use cases require specific installs and api keys:
 - _OpenSearch_:
   - Install requirements with `pip install opensearch-py`
   - If you want to set up OpenSearch on your local, [here](https://opensearch.org/docs/latest/)
+- _DeepLake_:
+  - Install requirements with `pip install deeplake`
 
 
 If you are using the `NLTKTextSplitter` or the `SpacyTextSplitter`, you will also need to install the appropriate models. For example, if you want to use the `SpacyTextSplitter`, you will need to install the `en_core_web_sm` model with `python -m spacy download en_core_web_sm`. Similarly, if you want to use the `NLTKTextSplitter`, you will need to install the `punkt` model with `python -m nltk.downloader punkt`.

docs/use_cases/question_answering.md

@@ -1,5 +1,41 @@
 # Question Answering
 
+Question answering in this context refers to question answering over your document data. 
+For question answering over other types of data, like [SQL databases](../modules/chains/examples/sqlite.html) or [APIs](../modules/chains/examples/api.html), please see [here](../modules/chains/utility_how_to.html)
+
+For question answering over many documents, you almost always want to create an index over the data.
+This can be used to smartly access the most relevant documents for a given question, allowing you to avoid having to pass all the documents to the LLM (saving you time and money).
+
+See [this notebook](../modules/indexes/getting_started.ipynb) for a more detailed introduction to this, but for a super quick start the steps involved are:
+
+**Load Your Documents**
+```python
+from langchain.document_loaders import TextLoader
+loader = TextLoader('../state_of_the_union.txt')
+```
+See [here](../modules/document_loaders/how_to_guides.rst) for more information on how to get started with document loading.
+
+**Create Your Index**
+```python
+from langchain.indexes import VectorstoreIndexCreator
+index = VectorstoreIndexCreator().from_loaders([loader])
+```
+The best and most popular index by far at the moment is the VectorStore index.
+
+**Query Your Index**
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+index.query(query)
+```
+Alternatively, use `query_with_sources` to also get back the sources involved
+```python
+query = "What did the president say about Ketanji Brown Jackson"
+index.query_with_sources(query)
+```
+Again, these high level interfaces obfuscate a lot of what is going on under the hood, so please see [this notebook](../modules/indexes/getting_started.ipynb) for a lower level walkthrough.
+
+## Document Question Answering
+
 Question answering involves fetching multiple documents, and then asking a question of them.
 The LLM response will contain the answer to your question, based on the content of the documents.
 
@@ -15,7 +51,7 @@ The following resources exist:
 - [Question Answering Notebook](/modules/indexes/chain_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
 - [VectorDB Question Answering Notebook](/modules/indexes/chain_examples/vector_db_qa.ipynb): A notebook walking through how to do question answering over a vector database. This can often be useful for when you have a LOT of documents, and you don't want to pass them all to the LLM, but rather first want to do some semantic search over embeddings.
 
-### Adding in sources
+## Adding in sources
 
 There is also a variant of this, where in addition to responding with the answer the language model will also cite its sources (eg which of the documents passed in it used).
 
@@ -31,7 +67,7 @@ The following resources exist:
 - [QA With Sources Notebook](/modules/indexes/chain_examples/qa_with_sources.ipynb): A notebook walking through how to accomplish this task.
 - [VectorDB QA With Sources Notebook](/modules/indexes/chain_examples/vector_db_qa_with_sources.ipynb): A notebook walking through how to do question answering with sources over a vector database. This can often be useful for when you have a LOT of documents, and you don't want to pass them all to the LLM, but rather first want to do some semantic search over embeddings.
 
-### Additional Related Resources
+## Additional Related Resources
 
 Additional related resources include:
 - [Utilities for working with Documents](/modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents) and Embeddings & Vectorstores (useful for the above Vector DB example).

docs/use_cases/summarization.md

@@ -15,6 +15,6 @@ The following resources exist:
 - [Summarization Notebook](../modules/indexes/chain_examples/summarize.ipynb): A notebook walking through how to accomplish this task.
 
 Additional related resources include:
-- [Utilities for working with Documents](../modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents).
+- [Utilities for working with Documents](../reference/utils.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents).
 - [CombineDocuments Chains](../modules/indexes/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
 - [Data Augmented Generation](./combine_docs.md): An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).

langchain/agents/__init__.py

@@ -1,5 +1,14 @@
 """Interface for agents."""
 from langchain.agents.agent import Agent, AgentExecutor
+from langchain.agents.agent_toolkits import (
+    create_csv_agent,
+    create_json_agent,
+    create_openapi_agent,
+    create_pandas_dataframe_agent,
+    create_sql_agent,
+    create_vectorstore_agent,
+    create_vectorstore_router_agent,
+)
 from langchain.agents.conversational.base import ConversationalAgent
 from langchain.agents.initialize import initialize_agent
 from langchain.agents.load_tools import get_all_tool_names, load_tools
@@ -24,4 +33,11 @@ __all__ = [
     "get_all_tool_names",
     "ConversationalAgent",
     "load_agent",
+    "create_sql_agent",
+    "create_json_agent",
+    "create_openapi_agent",
+    "create_vectorstore_router_agent",
+    "create_vectorstore_agent",
+    "create_pandas_dataframe_agent",
+    "create_csv_agent",
 ]

langchain/agents/agent.py

@@ -47,7 +47,7 @@ class Agent(BaseModel):
 
     @property
     def _stop(self) -> List[str]:
-        return [f"\n{self.observation_prefix}"]
+        return [f"\n{self.observation_prefix}", f"\n\t{self.observation_prefix}"]
 
     def _construct_scratchpad(
         self, intermediate_steps: List[Tuple[AgentAction, str]]
@@ -426,7 +426,7 @@ class AgentExecutor(Chain, BaseModel):
             )
         else:
             observation = InvalidTool().run(
-                output.tool_input,
+                output.tool,
                 verbose=self.verbose,
                 color=None,
                 llm_prefix="",
@@ -473,7 +473,7 @@ class AgentExecutor(Chain, BaseModel):
             )
         else:
             observation = await InvalidTool().arun(
-                output.tool_input,
+                output.tool,
                 verbose=self.verbose,
                 color=None,
                 llm_prefix="",

langchain/agents/agent_toolkits/__init__.py

@@ -0,0 +1,37 @@
+"""Agent toolkits."""
+
+from langchain.agents.agent_toolkits.csv.base import create_csv_agent
+from langchain.agents.agent_toolkits.json.base import create_json_agent
+from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
+from langchain.agents.agent_toolkits.openapi.base import create_openapi_agent
+from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
+from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
+from langchain.agents.agent_toolkits.python.base import create_python_agent
+from langchain.agents.agent_toolkits.sql.base import create_sql_agent
+from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
+from langchain.agents.agent_toolkits.vectorstore.base import (
+    create_vectorstore_agent,
+    create_vectorstore_router_agent,
+)
+from langchain.agents.agent_toolkits.vectorstore.toolkit import (
+    VectorStoreInfo,
+    VectorStoreRouterToolkit,
+    VectorStoreToolkit,
+)
+
+__all__ = [
+    "create_json_agent",
+    "create_sql_agent",
+    "create_openapi_agent",
+    "create_python_agent",
+    "create_vectorstore_agent",
+    "JsonToolkit",
+    "SQLDatabaseToolkit",
+    "OpenAPIToolkit",
+    "VectorStoreToolkit",
+    "create_vectorstore_router_agent",
+    "VectorStoreInfo",
+    "VectorStoreRouterToolkit",
+    "create_pandas_dataframe_agent",
+    "create_csv_agent",
+]

langchain/agents/agent_toolkits/base.py

@@ -0,0 +1,15 @@
+"""Toolkits for agents."""
+from abc import abstractmethod
+from typing import List
+
+from pydantic import BaseModel
+
+from langchain.tools import BaseTool
+
+
+class BaseToolkit(BaseModel):
+    """Class responsible for defining a collection of related tools."""
+
+    @abstractmethod
+    def get_tools(self) -> List[BaseTool]:
+        """Get the tools in the toolkit."""

langchain/agents/agent_toolkits/csv/__init__.py

@@ -0,0 +1 @@
+"""CSV toolkit."""

langchain/agents/agent_toolkits/csv/base.py

@@ -0,0 +1,17 @@
+"""Agent for working with csvs."""
+from typing import Any, Optional
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
+from langchain.llms.base import BaseLLM
+
+
+def create_csv_agent(
+    llm: BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any
+) -> AgentExecutor:
+    """Create csv agent by loading to a dataframe and using pandas agent."""
+    import pandas as pd
+
+    _kwargs = pandas_kwargs or {}
+    df = pd.read_csv(path, **_kwargs)
+    return create_pandas_dataframe_agent(llm, df, **kwargs)

langchain/agents/agent_toolkits/json/__init__.py

@@ -0,0 +1 @@
+"""Json agent."""

langchain/agents/agent_toolkits/json/base.py

@@ -0,0 +1,43 @@
+"""Json agent."""
+from typing import Any, List, Optional
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
+from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
+from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains.llm import LLMChain
+from langchain.llms.base import BaseLLM
+
+
+def create_json_agent(
+    llm: BaseLLM,
+    toolkit: JsonToolkit,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    prefix: str = JSON_PREFIX,
+    suffix: str = JSON_SUFFIX,
+    format_instructions: str = FORMAT_INSTRUCTIONS,
+    input_variables: Optional[List[str]] = None,
+    verbose: bool = False,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Construct a json agent from an LLM and tools."""
+    tools = toolkit.get_tools()
+    prompt = ZeroShotAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        format_instructions=format_instructions,
+        input_variables=input_variables,
+    )
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        callback_manager=callback_manager,
+    )
+    tool_names = [tool.name for tool in tools]
+    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+    return AgentExecutor.from_agent_and_tools(
+        agent=agent, tools=toolkit.get_tools(), verbose=verbose
+    )

langchain/agents/agent_toolkits/json/prompt.py

@@ -0,0 +1,25 @@
+# flake8: noqa
+
+JSON_PREFIX = """You are an agent designed to interact with JSON.
+Your goal is to return a final answer by interacting with the JSON.
+You have access to the following tools which help you learn more about the JSON you are interacting with.
+Only use the below tools. Only use the information returned by the below tools to construct your final answer.
+Do not make up any information that is not contained in the JSON.
+Your input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. 
+You should only use keys that you know for a fact exist. You must validate that a key exists by seeing it previously when calling `json_spec_list_keys`. 
+If you have not seen a key in one of those responses, you cannot use it.
+You should only add one key at a time to the path. You cannot add multiple keys at once.
+If you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.
+
+If the question does not seem to be related to the JSON, just return "I don't know" as the answer.
+Always begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.
+
+Note that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".
+In this case, you should ALWAYS follow up by using the `json_spec_list_keys` tool to see what keys exist at that path.
+Do not simply refer the user to the JSON or a section of the JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.
+"""
+JSON_SUFFIX = """Begin!"
+
+Question: {input}
+Thought: I should look at the keys that exist in data to see what I have access to
+{agent_scratchpad}"""

langchain/agents/agent_toolkits/json/toolkit.py

@@ -0,0 +1,21 @@
+"""Toolkit for interacting with a JSON spec."""
+from __future__ import annotations
+
+from typing import List
+
+from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.tools import BaseTool
+from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec
+
+
+class JsonToolkit(BaseToolkit):
+    """Toolkit for interacting with a JSON spec."""
+
+    spec: JsonSpec
+
+    def get_tools(self) -> List[BaseTool]:
+        """Get the tools in the toolkit."""
+        return [
+            JsonListKeysTool(spec=self.spec),
+            JsonGetValueTool(spec=self.spec),
+        ]

langchain/agents/agent_toolkits/openapi/__init__.py

@@ -0,0 +1 @@
+"""OpenAPI spec agent."""

langchain/agents/agent_toolkits/openapi/base.py

@@ -0,0 +1,46 @@
+"""OpenAPI spec agent."""
+from typing import Any, List, Optional
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.openapi.prompt import (
+    OPENAPI_PREFIX,
+    OPENAPI_SUFFIX,
+)
+from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
+from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains.llm import LLMChain
+from langchain.llms.base import BaseLLM
+
+
+def create_openapi_agent(
+    llm: BaseLLM,
+    toolkit: OpenAPIToolkit,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    prefix: str = OPENAPI_PREFIX,
+    suffix: str = OPENAPI_SUFFIX,
+    format_instructions: str = FORMAT_INSTRUCTIONS,
+    input_variables: Optional[List[str]] = None,
+    verbose: bool = False,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Construct a json agent from an LLM and tools."""
+    tools = toolkit.get_tools()
+    prompt = ZeroShotAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        format_instructions=format_instructions,
+        input_variables=input_variables,
+    )
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        callback_manager=callback_manager,
+    )
+    tool_names = [tool.name for tool in tools]
+    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+    return AgentExecutor.from_agent_and_tools(
+        agent=agent, tools=toolkit.get_tools(), verbose=verbose
+    )

langchain/agents/agent_toolkits/openapi/prompt.py

@@ -0,0 +1,29 @@
+# flake8: noqa
+
+OPENAPI_PREFIX = """You are an agent designed to answer questions by making web requests to an API given the openapi spec.
+
+If the question does not seem related to the API, return I don't know. Do not make up an answer.
+Only use information provided by the tools to construct your response.
+
+First, find the base URL needed to make the request.
+
+Second, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.
+
+Third, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.
+
+Fourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.
+
+Use the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.
+If you get a not found error, ensure that you are using a path that actually exists in the spec.
+"""
+OPENAPI_SUFFIX = """Begin!"
+
+Question: {input}
+Thought: I should explore the spec to find the base url for the API.
+{agent_scratchpad}"""
+
+DESCRIPTION = """Can be used to answer questions about the openapi spec for the API. Always use this tool before trying to make a request. 
+Example inputs to this tool: 
+    'What are the required query parameters for a GET request to the /bar endpoint?`
+    'What are the required parameters in the request body for a POST request to the /foo endpoint?'
+Always give this tool a specific question."""

langchain/agents/agent_toolkits/openapi/toolkit.py

@@ -0,0 +1,67 @@
+"""Requests toolkit."""
+from __future__ import annotations
+
+from typing import Any, List
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.agents.agent_toolkits.json.base import create_json_agent
+from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
+from langchain.agents.agent_toolkits.openapi.prompt import DESCRIPTION
+from langchain.agents.tools import Tool
+from langchain.llms.base import BaseLLM
+from langchain.requests import RequestsWrapper
+from langchain.tools import BaseTool
+from langchain.tools.json.tool import JsonSpec
+from langchain.tools.requests.tool import (
+    RequestsDeleteTool,
+    RequestsGetTool,
+    RequestsPatchTool,
+    RequestsPostTool,
+    RequestsPutTool,
+)
+
+
+class RequestsToolkit(BaseToolkit):
+    """Toolkit for making requests."""
+
+    requests_wrapper: RequestsWrapper
+
+    def get_tools(self) -> List[BaseTool]:
+        """Return a list of tools."""
+        return [
+            RequestsGetTool(requests_wrapper=self.requests_wrapper),
+            RequestsPostTool(requests_wrapper=self.requests_wrapper),
+            RequestsPatchTool(requests_wrapper=self.requests_wrapper),
+            RequestsPutTool(requests_wrapper=self.requests_wrapper),
+            RequestsDeleteTool(requests_wrapper=self.requests_wrapper),
+        ]
+
+
+class OpenAPIToolkit(BaseToolkit):
+    """Toolkit for interacting with a OpenAPI api."""
+
+    json_agent: AgentExecutor
+    requests_wrapper: RequestsWrapper
+
+    def get_tools(self) -> List[BaseTool]:
+        """Get the tools in the toolkit."""
+        json_agent_tool = Tool(
+            name="json_explorer",
+            func=self.json_agent.run,
+            description=DESCRIPTION,
+        )
+        request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
+        return [*request_toolkit.get_tools(), json_agent_tool]
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLLM,
+        json_spec: JsonSpec,
+        requests_wrapper: RequestsWrapper,
+        **kwargs: Any,
+    ) -> OpenAPIToolkit:
+        """Create json agent from llm, then initialize."""
+        json_agent = create_json_agent(llm, JsonToolkit(spec=json_spec), **kwargs)
+        return cls(json_agent=json_agent, requests_wrapper=requests_wrapper)

langchain/agents/agent_toolkits/pandas/__init__.py

@@ -0,0 +1 @@
+"""Pandas toolkit."""

langchain/agents/agent_toolkits/pandas/base.py

@@ -0,0 +1,42 @@
+"""Agent for working with pandas objects."""
+from typing import Any, List, Optional
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
+from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains.llm import LLMChain
+from langchain.llms.base import BaseLLM
+from langchain.tools.python.tool import PythonAstREPLTool
+
+
+def create_pandas_dataframe_agent(
+    llm: BaseLLM,
+    df: Any,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    prefix: str = PREFIX,
+    suffix: str = SUFFIX,
+    input_variables: Optional[List[str]] = None,
+    verbose: bool = False,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Construct a pandas agent from an LLM and dataframe."""
+    import pandas as pd
+
+    if not isinstance(df, pd.DataFrame):
+        raise ValueError(f"Expected pandas object, got {type(df)}")
+    if input_variables is None:
+        input_variables = ["df", "input", "agent_scratchpad"]
+    tools = [PythonAstREPLTool(locals={"df": df})]
+    prompt = ZeroShotAgent.create_prompt(
+        tools, prefix=prefix, suffix=suffix, input_variables=input_variables
+    )
+    partial_prompt = prompt.partial(df=str(df.head()))
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=partial_prompt,
+        callback_manager=callback_manager,
+    )
+    tool_names = [tool.name for tool in tools]
+    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+    return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)

langchain/agents/agent_toolkits/pandas/prompt.py

@@ -0,0 +1,13 @@
+# flake8: noqa
+
+PREFIX = """
+You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
+You should use the tools below to answer the question posed of you:"""
+
+SUFFIX = """
+This is the result of `print(df.head())`:
+{df}
+
+Begin!
+Question: {input}
+{agent_scratchpad}"""