cr

.github/workflows/test.yml

@@ -31,4 +31,4 @@ jobs:
         run: poetry install
       - name: Run unit tests
         run: |
-          make test
+          make tests

.gitignore

@@ -106,7 +106,6 @@ celerybeat.pid
 
 # Environments
 .env
-.envrc
 .venv
 .venvs
 env/
@@ -135,9 +134,3 @@ dmypy.json
 
 # macOS display setting files
 .DS_Store
-
-# Wandb directory
-wandb/
-
-# asdf tool versions
-.tool-versions

CONTRIBUTING.md

@@ -73,14 +73,10 @@ poetry install -E all
 
 This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
 
-❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
-
 Now, you should be able to run the common tasks in the following section.
 
 ## ✅Common Tasks
 
-Type `make` for a list of common tasks.
-
 ### Code Formatting
 
 Formatting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/).
@@ -120,7 +116,7 @@ Unit tests cover modular logic that does not require calls to outside APIs.
 To run unit tests:
 
 ```bash
-make test
+make tests
 ```
 
 If you add new logic, please add a unit test.

Makefile

@@ -1,15 +1,11 @@
-.PHONY: all clean format lint test tests test_watch integration_tests help
+.PHONY: format lint tests tests_watch integration_tests
 
-all: help
-	
 coverage:
 	poetry run pytest --cov \
 		--cov-config=.coveragerc \
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
-clean: docs_clean
-
 docs_build:
 	cd docs && poetry run make html
 
@@ -21,33 +17,19 @@ docs_linkcheck:
 
 format:
 	poetry run black .
-	poetry run ruff --select I --fix .
+	poetry run isort .
 
 lint:
 	poetry run mypy .
 	poetry run black . --check
-	poetry run ruff .
-
-test:
-	poetry run pytest tests/unit_tests
+	poetry run isort . --check
+	poetry run flake8 .
 
 tests:
 	poetry run pytest tests/unit_tests
 
-test_watch:
+tests_watch:
 	poetry run ptw --now . -- tests/unit_tests
 
 integration_tests:
 	poetry run pytest tests/integration_tests
-
-help:
-	@echo '----'
-	@echo 'coverage            - run unit tests and generate coverage report'
-	@echo 'docs_build          - build the documentation'
-	@echo 'docs_clean          - clean the documentation build artifacts'
-	@echo 'docs_linkcheck      - run linkchecker on the documentation'
-	@echo 'format              - run code formatters'
-	@echo 'lint                - run linters'
-	@echo 'test                - run unit tests'
-	@echo 'test_watch          - run unit tests in watch mode'
-	@echo 'integration_tests   - run integration 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?
 
@@ -79,4 +79,4 @@ For more information on these concepts, please see our [full documentation](http
 
 As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
-For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](CONTRIBUTING.md).

docs/_static/css/custom.css

@@ -1,13 +1,3 @@
 pre {
-  white-space: break-spaces;
-}
-
-@media (min-width: 1200px) {
-  .container,
-  .container-lg,
-  .container-md,
-  .container-sm,
-  .container-xl {
-    max-width: 2560px !important;
-  }
-}
+    white-space: break-spaces;
+}

docs/conf.py

@@ -23,14 +23,13 @@ with open("../pyproject.toml") as f:
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, Harrison Chase"
+copyright = "2022, Harrison Chase"
 author = "Harrison Chase"
 
 version = data["tool"]["poetry"]["version"]
 release = version
 
 html_title = project + " " + version
-html_last_updated_fmt = "%b %d, %Y"
 
 
 # -- General configuration ---------------------------------------------------
@@ -46,7 +45,6 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
     "myst_nb",
-    "sphinx_copybutton",
     "sphinx_panels",
     "IPython.sphinxext.ipython_console_highlighting",
 ]

docs/deployments.md

@@ -32,8 +32,3 @@ It implements a Question Answering app and contains instructions for deploying t
 ## [Vercel](https://github.com/homanp/vercel-langchain)
 
 A minimal example on how to run LangChain on Vercel using Flask.
-
-
-## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
-This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
-This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.

docs/ecosystem/atlas.md

@@ -1,27 +0,0 @@
-# AtlasDB
-
-This page covers how to use 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 Atlas 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 AtlasDB wrapper, see [this notebook](../modules/indexes/vectorstore_examples/atlas.ipynb)

docs/ecosystem/bananadev.md

@@ -1,79 +0,0 @@
-# Banana
-
-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 `pip install banana-dev`
-- 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/).
-You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
-
-## Build the Banana app
-
-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
-    global tokenizer
-
-    # Parse out your arguments
-    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,
-        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
-        )
-
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-    # Return the results as a dictionary
-    result = {'output': result}
-    return result
-```
-
-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
-
-```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/cerebriumai.md

@@ -1,17 +0,0 @@
-# CerebriumAI
-
-This page covers how to use the CerebriumAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
-
-## Installation and Setup
-- Install with `pip install cerebrium`
-- Get an CerebriumAI api key and set it as an environment variable (`CEREBRIUMAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an CerebriumAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import CerebriumAI
-```

docs/ecosystem/chroma.md

@@ -1,20 +0,0 @@
-# Chroma
-
-This page covers how to use the Chroma ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install chromadb`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Chroma
-```
-
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/cohere.md

@@ -22,4 +22,4 @@ There exists an Cohere Embeddings wrapper, which you can access with
 ```python
 from langchain.embeddings import CohereEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/utils/combine_docs_examples/embeddings.ipynb)

docs/ecosystem/deepinfra.md

@@ -1,17 +0,0 @@
-# DeepInfra
-
-This page covers how to use the DeepInfra ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers.
-
-## Installation and Setup
-- Get your DeepInfra api key from this link [here](https://deepinfra.com/).
-- Get an DeepInfra api key and set it as an environment variable (`DEEPINFRA_API_TOKEN`)
-
-## Wrappers
-
-### LLM
-
-There exists an DeepInfra LLM wrapper, which you can access with
-```python
-from langchain.llms import DeepInfra
-```

docs/ecosystem/deeplake.md

@@ -1,25 +0,0 @@
-# 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/ecosystem/forefrontai.md

@@ -1,16 +0,0 @@
-# ForefrontAI
-
-This page covers how to use the ForefrontAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
-
-## Installation and Setup
-- Get an ForefrontAI api key and set it as an environment variable (`FOREFRONTAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an ForefrontAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import ForefrontAI
-```

docs/ecosystem/google_serper.md

@@ -1,72 +0,0 @@
-# Google Serper Wrapper
-
-This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. 
-It is broken into two parts: setup, and then references to the specific Google Serper wrapper.
-
-## Setup
-- Go to [serper.dev](https://serper.dev) to sign up for a free account
-- Get the api key and set it as an environment variable (`SERPER_API_KEY`)
-
-## Wrappers
-
-### Utility
-
-There exists a GoogleSerperAPIWrapper utility which wraps this API. To import this utility:
-
-```python
-from langchain.utilities import GoogleSerperAPIWrapper
-```
-
-You can use it as part of a Self Ask chain:
-
-```python
-from langchain.utilities import GoogleSerperAPIWrapper
-from langchain.llms.openai import OpenAI
-from langchain.agents import initialize_agent, Tool
-
-import os
-
-os.environ["SERPER_API_KEY"] = ""
-os.environ['OPENAI_API_KEY'] = ""
-
-llm = OpenAI(temperature=0)
-search = GoogleSerperAPIWrapper()
-tools = [
-    Tool(
-        name="Intermediate Answer",
-        func=search.run,
-        description="useful for when you need to ask with search"
-    )
-]
-
-self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True)
-self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
-```
-
-#### Output
-```
-Entering new AgentExecutor chain...
- Yes.
-Follow up: Who is the reigning men's U.S. Open champion?
-Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
-Follow up: Where is Carlos Alcaraz from?
-Intermediate answer: El Palmar, Spain
-So the final answer is: El Palmar, Spain
-
-> Finished chain.
-
-'El Palmar, Spain'
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_serper.ipynb).
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-```python
-from langchain.agents import load_tools
-tools = load_tools(["google-serper"])
-```
-
-For more information on this, see [this page](../modules/agents/tools.md)

docs/ecosystem/gooseai.md

@@ -1,23 +0,0 @@
-# GooseAI
-
-This page covers how to use the GooseAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install openai`
-- Get your GooseAI api key from this link [here](https://goose.ai/).
-- Set the environment variable (`GOOSEAI_API_KEY`).
-
-```python
-import os
-os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY"
-```
-
-## Wrappers
-
-### LLM
-
-There exists an GooseAI LLM wrapper, which you can access with: 
-```python
-from langchain.llms import GooseAI
-```

docs/ecosystem/graphsignal.md

@@ -1,38 +0,0 @@
-# Graphsignal
-
-This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
-
-## Installation and Setup
-
-- Install the Python library with `pip install graphsignal`
-- Create free Graphsignal account [here](https://graphsignal.com)
-- Get an API key and set it as an environment variable (`GRAPHSIGNAL_API_KEY`)
-
-## Tracing and Monitoring
-
-Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, metrics and errors are then available in your [Graphsignal dashboard](https://app.graphsignal.com/). No prompts or other sensitive data are sent to Graphsignal cloud, only statistics and metadata.
-
-Initialize the tracer by providing a deployment name:
-
-```python
-import graphsignal
-
-graphsignal.configure(deployment='my-langchain-app-prod')
-```
-
-In order to trace full runs and see a breakdown by chains and tools, you can wrap the calling routine or use a decorator:
-
-```python
-with graphsignal.start_trace('my-chain'):
-    chain.run("some initial text")
-```
-
-Optionally, enable profiling to record function-level statistics for each trace.
-
-```python
-with graphsignal.start_trace(
-        'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)):
-    chain.run("some initial text")
-```
-
-See the [Quick Start](https://graphsignal.com/docs/guides/quick-start/) guide for complete setup instructions.

docs/ecosystem/helicone.md

@@ -1,53 +0,0 @@
-# Helicone
-
-This page covers how to use the [Helicone](https://helicone.ai) ecosystem within LangChain.
-
-## What is Helicone?
-
-Helicone is an [open source](https://github.com/Helicone/helicone) observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
-
-![Helicone](../_static/HeliconeDashboard.png)
-
-## Quick start
-
-With your LangChain environment you can just add the following parameter.
-
-```bash
-export OPENAI_API_BASE="https://oai.hconeai.com/v1"
-```
-
-Now head over to [helicone.ai](https://helicone.ai/onboarding?step=2) to create your account, and add your OpenAI API key within our dashboard to view your logs.
-
-![Helicone](../_static/HeliconeKeys.png)
-
-## How to enable Helicone caching
-
-```python
-from langchain.llms import OpenAI
-import openai
-openai.api_base = "https://oai.hconeai.com/v1"
-
-llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"})
-text = "What is a helicone?"
-print(llm(text))
-```
-
-[Helicone caching docs](https://docs.helicone.ai/advanced-usage/caching)
-
-## How to use Helicone custom properties
-
-```python
-from langchain.llms import OpenAI
-import openai
-openai.api_base = "https://oai.hconeai.com/v1"
-
-llm = OpenAI(temperature=0.9, headers={
-        "Helicone-Property-Session": "24",
-        "Helicone-Property-Conversation": "support_issue_2",
-        "Helicone-Property-App": "mobile",
-      })
-text = "What is a helicone?"
-print(llm(text))
-```
-
-[Helicone property docs](https://docs.helicone.ai/advanced-usage/custom-properties)

docs/ecosystem/huggingface.md

@@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.embeddings import HuggingFaceHubEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/utils/combine_docs_examples/embeddings.ipynb)
 
 ### Tokenizer
 
@@ -59,7 +59,7 @@ You can also use it to count tokens when splitting documents with
 from langchain.text_splitter import CharacterTextSplitter
 CharacterTextSplitter.from_huggingface_tokenizer(...)
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/utils/combine_docs_examples/textsplitter.ipynb)
 
 
 ### Datasets

docs/ecosystem/modal.md

@@ -1,66 +0,0 @@
-# Modal
-
-This page covers how to use the Modal ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
-
-## Installation and Setup
-- Install with `pip install modal-client`
-- Run `modal token new`
-
-## Define your Modal Functions and Webhooks
-
-You must include a prompt. There is a rigid response structure.
-
-```python
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def my_webhook(item: Item):
-    return {"prompt": my_function.call(item.prompt)}
-```
-
-An example with GPT2:
-
-```python
-from pydantic import BaseModel
-
-import modal
-
-stub = modal.Stub("example-get-started")
-
-volume = modal.SharedVolume().persist("gpt2_model_vol")
-CACHE_PATH = "/root/model_cache"
-
-@stub.function(
-    gpu="any",
-    image=modal.Image.debian_slim().pip_install(
-        "tokenizers", "transformers", "torch", "accelerate"
-    ),
-    shared_volumes={CACHE_PATH: volume},
-    retries=3,
-)
-def run_gpt2(text: str):
-    from transformers import GPT2Tokenizer, GPT2LMHeadModel
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2LMHeadModel.from_pretrained('gpt2')
-    encoded_input = tokenizer(text, return_tensors='pt').input_ids
-    output = model.generate(encoded_input, max_length=50, do_sample=True)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def get_text(item: Item):
-    return {"prompt": run_gpt2.call(item.prompt)}
-```
-
-## Wrappers
-
-### LLM
-
-There exists an Modal LLM wrapper, which you can access with 
-```python
-from langchain.llms import Modal
-```

docs/ecosystem/openai.md

@@ -31,7 +31,7 @@ There exists an OpenAI Embeddings wrapper, which you can access with
 ```python
 from langchain.embeddings import OpenAIEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/utils/combine_docs_examples/embeddings.ipynb)
 
 
 ### Tokenizer
@@ -44,7 +44,7 @@ You can also use it to count tokens when splitting documents with
 from langchain.text_splitter import CharacterTextSplitter
 CharacterTextSplitter.from_tiktoken_encoder(...)
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/utils/combine_docs_examples/textsplitter.ipynb)
 
 ### Moderation
 You can also access the OpenAI content moderation endpoint with

docs/ecosystem/opensearch.md

@@ -1,21 +0,0 @@
-# OpenSearch
-
-This page covers how to use the OpenSearch ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install opensearch-py`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore 
-for semantic search using approximate vector search powered by lucene, nmslib and faiss engines 
-or using painless scripting and script scoring functions for bruteforce vector search.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import OpenSearchVectorSearch
-```
-
-For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)

docs/ecosystem/petals.md

@@ -1,17 +0,0 @@
-# Petals
-
-This page covers how to use the Petals ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
-
-## Installation and Setup
-- Install with `pip install petals`
-- Get a Hugging Face api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Petals LLM wrapper, which you can access with 
-```python
-from langchain.llms import Petals
-```

docs/ecosystem/pgvector.md

@@ -1,29 +0,0 @@
-# PGVector
-
-This page covers how to use the Postgres [PGVector](https://github.com/pgvector/pgvector) ecosystem within LangChain
-It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
-
-## Installation
-- Install the Python package with `pip install pgvector`
-
-
-## Setup
-1. The first step is to create a database with the `pgvector` extension installed.
-
-    Follow the steps at [PGVector Installation Steps](https://github.com/pgvector/pgvector#installation) to install the database and the extension. The docker image is the easiest way to get started.
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores.pgvector import PGVector
-```
-
-### Usage
-
-For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](../modules/indexes/vectorstore_examples/pgvector.ipynb)

docs/ecosystem/pinecone.md

@@ -17,4 +17,4 @@ To import this vectorstore:
 from langchain.vectorstores import Pinecone
 ```
 
-For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/vectorstore_examples/pinecone.ipynb)
+For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/utils/combine_docs_examples/vectorstores.ipynb)

docs/ecosystem/promptlayer.md

@@ -1,49 +0,0 @@
-# PromptLayer
-
-This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
-
-## Installation and Setup
-
-If you want to work with PromptLayer:
-- Install the promptlayer python library `pip install promptlayer`
-- Create a PromptLayer account
-- Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an PromptLayer OpenAI LLM wrapper, which you can access with
-```python
-from langchain.llms import PromptLayerOpenAI
-```
-
-To tag your requests, use the argument `pl_tags` when instanializing the LLM
-```python
-from langchain.llms import PromptLayerOpenAI
-llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
-```
-
-To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
-```python
-from langchain.llms import PromptLayerOpenAI
-llm = PromptLayerOpenAI(return_pl_id=True)
-```
-This will add the PromptLayer request ID in the `generation_info` field of the `Generation` returned when using `.generate` or `.agenerate`
-
-For example:
-```python
-llm_results = llm.generate(["hello world"])
-for res in llm_results.generations:
-    print("pl request id: ", res[0].generation_info["pl_request_id"])
-```
-You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
-
-This LLM is identical to the [OpenAI LLM](./openai), except that
-- all your requests will be logged to your PromptLayer account
-- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
-- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
-
-
-PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/chat/examples/promptlayer_chat_openai.ipynb) and `PromptLayerOpenAIChat`

docs/ecosystem/qdrant.md

@@ -1,20 +0,0 @@
-# Qdrant
-
-This page covers how to use the Qdrant ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install qdrant-client`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Qdrant
-```
-
-For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](../modules/indexes/vectorstore_examples/qdrant.ipynb)

docs/ecosystem/runhouse.md

@@ -1,31 +0,0 @@
-# Runhouse
-
-This page covers how to use the [Runhouse](https://github.com/run-house/runhouse) ecosystem within LangChain.
-It is broken into three parts: installation and setup, LLMs, and Embeddings.
-
-## Installation and Setup
-- Install the Python SDK with `pip install runhouse`
-- If you'd like to use on-demand cluster, check your cloud credentials with `sky check`
-
-## Self-hosted LLMs
-For a basic self-hosted LLM, you can use the `SelfHostedHuggingFaceLLM` class. For more
-custom LLMs, you can use the `SelfHostedPipeline` parent class.
-
-```python
-from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
-```
-
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
-
-## Self-hosted Embeddings
-There are several ways to use self-hosted embeddings with LangChain via Runhouse.
-
-For a basic self-hosted embedding from a Hugging Face Transformers model, you can use 
-the `SelfHostedEmbedding` class.
-```python
-from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
-```
-
-For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
-
-##

docs/ecosystem/searx.md

@@ -1,58 +0,0 @@
-# SearxNG Search API
-
-This page covers how to use the SearxNG search API within LangChain.
-It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
-
-## Installation and Setup
-
-While it is possible to utilize the wrapper in conjunction with  [public searx
-instances](https://searx.space/) these instances frequently do not permit API
-access (see note on output format below) and have limitations on the frequency
-of requests. It is recommended to opt for a self-hosted instance instead.
-
-### Self Hosted Instance:
-
-See [this page](https://searxng.github.io/searxng/admin/installation.html) for installation instructions.
-
-When you install SearxNG, the only active output format by default is the HTML format.
-You need to activate the `json` format to use the API. This can be done by adding the following line to the `settings.yml` file:
-```yaml
-search:
-    formats:
-        - html
-        - json
-```
-You can make sure that the API is working by issuing a curl request to the API endpoint:
-
-`curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888`
-
-This should return a JSON object with the results.
-
-
-## Wrappers
-
-### Utility
-
-To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with:
-    1. the named parameter `searx_host` when creating the instance.
-    2. exporting the environment variable `SEARXNG_HOST`.
-
-You can use the wrapper to get results from a SearxNG instance. 
-
-```python
-from langchain.utilities import SearxSearchWrapper
-s = SearxSearchWrapper(searx_host="http://localhost:8888")
-s.run("what is a large language model?")
-```
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-
-```python
-from langchain.agents import load_tools
-tools = load_tools(["searx-search"], searx_host="http://localhost:8888")
-```
-
-For more information on tools, see [this page](../modules/agents/tools.md)

docs/ecosystem/stochasticai.md

@@ -1,17 +0,0 @@
-# StochasticAI
-
-This page covers how to use the StochasticAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
-
-## Installation and Setup
-- Install with `pip install stochasticx`
-- Get an StochasticAI api key and set it as an environment variable (`STOCHASTICAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an StochasticAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import StochasticAI
-```

docs/ecosystem/unstructured.md

@@ -1,44 +0,0 @@
-# Unstructured
-
-This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
-ecosystem within LangChain. The `unstructured` package from
-[Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like
-PDFs and Word documents.
-
-
-This page is broken into two parts: installation and setup, and then references to specific
-`unstructured` wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install "unstructured[local-inference]"`
-- Install the following system dependencies if they are not already available on your system.
-  Depending on what document types you're parsing, you may not need all of these.
-    - `libmagic-dev`
-    - `poppler-utils`
-    - `tesseract-ocr`
-    - `libreoffice`
-- If you are parsing PDFs using the `"hi_res"` strategy, run the following to install the `detectron2` model, which
-  `unstructured` uses for layout detection:
-    - `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"`
-    - If `detectron2` is not installed, `unstructured` will fallback to processing PDFs
-      using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
-      `detectron2`.
-
-## Wrappers
-
-### Data Loaders
-
-The primary `unstructured` wrappers within `langchain` are data loaders. The following
-shows how to use the most basic unstructured data loader. There are other file-specific
-data loaders available in the `langchain.document_loaders` module.
-
-```python
-from langchain.document_loaders import UnstructuredFileLoader
-
-loader = UnstructuredFileLoader("state_of_the_union.txt")
-loader.load()
-```
-
-If you instantiate the loader with `UnstructuredFileLoader(mode="elements")`, the loader
-will track additional metadata like the page number and text type (i.e. title, narrative text)
-when that information is available.

docs/ecosystem/wandb_tracking.ipynb

@@ -1,625 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Weights & Biases\n",
-    "\n",
-    "This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.\n",
-    "\n",
-    "Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing\n",
-    "\n",
-    "View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B--VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install wandb\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "T1bSmKd6V2If"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"WANDB_API_KEY\"] = \"\"\n",
-    "# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "# os.environ[\"SERPAPI_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "8WAGnTWpUUnD"
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.callbacks.base import CallbackManager\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "Callback Handler that logs to Weights and Biases.\n",
-    "\n",
-    "Parameters:\n",
-    "    job_type (str): The type of job.\n",
-    "    project (str): The project to log to.\n",
-    "    entity (str): The entity to log to.\n",
-    "    tags (list): The tags to log.\n",
-    "    group (str): The group to log to.\n",
-    "    name (str): The name of the run.\n",
-    "    notes (str): The notes to log.\n",
-    "    visualize (bool): Whether to visualize the run.\n",
-    "    complexity_metrics (bool): Whether to log complexity metrics.\n",
-    "    stream_logs (bool): Whether to stream callback actions to W&B\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cxBFfZR8d9FC"
-   },
-   "source": [
-    "```\n",
-    "Default values for WandbCallbackHandler(...)\n",
-    "\n",
-    "visualize: bool = False,\n",
-    "complexity_metrics: bool = False,\n",
-    "stream_logs: bool = False,\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "KAz8weWuUeXF"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mharrison-chase\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"Main function.\n",
-    "\n",
-    "This function is used to try the callback handler.\n",
-    "Scenarios:\n",
-    "1. OpenAI LLM\n",
-    "2. Chain with multiple SubChains on multiple generations\n",
-    "3. Agent with Tools\n",
-    "\"\"\"\n",
-    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
-    "wandb_callback = WandbCallbackHandler(\n",
-    "    job_type=\"inference\",\n",
-    "    project=\"langchain_callback_demo\",\n",
-    "    group=f\"minimal_{session_group}\",\n",
-    "    name=\"llm\",\n",
-    "    tags=[\"test\"],\n",
-    ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Q-65jwrDeK6w"
-   },
-   "source": [
-    "\n",
-    "\n",
-    "```\n",
-    "# Defaults for WandbCallbackHandler.flush_tracker(...)\n",
-    "\n",
-    "reset: bool = True,\n",
-    "finish: bool = False,\n",
-    "```\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `flush_tracker` function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "o_VmneyIUyx8"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d7b4307ccdb450ea631497174fca2d1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016745895149999985, max=1.0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# SCENARIO 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "wandb_callback.flush_tracker(llm, name=\"simple_sequential\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "trxslyb1U28Y"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "uauQk10SUzF6"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbdbf28fb8ed40a3a60218d2e6d1a987",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016736786816666675, max=1.0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# SCENARIO 2 - Chain\n",
-    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\n",
-    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
-    "    },\n",
-    "    {\"title\": \"cocaine bear vs heroin wolf\"},\n",
-    "    {\"title\": \"the best in class mlops tooling\"},\n",
-    "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
-    "wandb_callback.flush_tracker(synopsis_chain, name=\"agent\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "_jN73xcPVEpI"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "Gpq4rk6VT9cu"
-   },
-   "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 who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.43 power.\n",
-      "Action: Calculator\n",
-      "Action Input: 26^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "wandb_callback.flush_tracker(agent, reset=False, finish=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "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": 1
-}

docs/ecosystem/weaviate.md

@@ -30,4 +30,4 @@ To import this vectorstore:
 from langchain.vectorstores import Weaviate
 ```
 
-For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)
+For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/utils/combine_docs_examples/vectorstores.ipynb)

docs/ecosystem/writer.md

@@ -1,16 +0,0 @@
-# Writer
-
-This page covers how to use the Writer ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
-
-## Installation and Setup
-- Get an Writer api key and set it as an environment variable (`WRITER_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Writer LLM wrapper, which you can access with 
-```python
-from langchain.llms import Writer
-```

docs/gallery.rst

@@ -37,17 +37,6 @@ Open Source
 
     ---
 
-    .. link-button:: https://github.com/normandmickey/MrsStax
-        :type: url
-        :text: QA Slack Bot
-        :classes: stretched-link btn-lg
-
-    +++
-
-    This application is a Slack Bot that uses Langchain and OpenAI's GPT3 language model to provide domain specific answers. You provide the documents.
-
-    ---
-
     .. link-button:: https://github.com/OpenBioLink/ThoughtSource
         :type: url
         :text: ThoughtSource
@@ -158,14 +147,14 @@ Open Source
 
     ---
 
-    .. link-button:: https://github.com/jerryjliu/llama_index
+    .. link-button:: https://github.com/jerryjliu/gpt_index
         :type: url
-        :text: LlamaIndex
+        :text: GPT Index
         :classes: stretched-link btn-lg
     
     +++
 
-    LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
+    GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
 
     ---
 
@@ -322,14 +311,5 @@ Proprietary
 
     By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for `YouTube videos <https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_, and then another followup added it for `Wikipedia <https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_.
 
-    ---
 
-    .. link-button:: https://mynd.so
-        :type: url
-        :text: Mynd
-        :classes: stretched-link btn-lg
 
-    +++
-
-    A journaling app for self-care that uses AI to uncover insights and patterns over time.
-    

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 for a company that makes colorful socks?"
+text = "What would be a good company name a company that makes colorful socks?"
 print(llm(text))
 ```
 

docs/index.rst

@@ -42,7 +42,7 @@ Checkout the below guide for a walkthrough of how to get started using LangChain
 Modules
 -----------
 
-There are several main modules that LangChain provides support for.
+There are six main modules that LangChain provides support for.
 For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
 These modules are, in increasing order of complexity:
 
@@ -57,14 +57,10 @@ These modules are, in increasing order of complexity:
 
 - `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
-- `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
-
 - `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
 
 - `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
-- `Chat <./modules/chat.html>`_: Chat models are a variation on Language Models that expose a different API - rather than working with raw text, they work with messages. LangChain provides a standard interface for working with them and doing all the same things as above.
-
 
 .. toctree::
    :maxdepth: 1
@@ -76,11 +72,9 @@ These modules are, in increasing order of complexity:
    ./modules/llms.md
    ./modules/document_loaders.md
    ./modules/utils.md
-   ./modules/indexes.md
    ./modules/chains.md
    ./modules/agents.md
    ./modules/memory.md
-   ./modules/chat.md
 
 Use Cases
 ----------
@@ -97,8 +91,6 @@ The above modules can be used in a variety of ways. LangChain also provides guid
 
 - `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
 
-- `Querying Tabular Data <./use_cases/tabular.html>`_: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
-
 - `Evaluation <./use_cases/evaluation.html>`_: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
 
 - `Generate similar examples <./use_cases/generate_examples.html>`_: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.
@@ -119,8 +111,6 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    ./use_cases/combine_docs.md
    ./use_cases/question_answering.md
    ./use_cases/summarization.md
-   ./use_cases/tabular.rst
-   ./use_cases/extraction.md
    ./use_cases/evaluation.rst
    ./use_cases/model_laboratory.ipynb
 

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's input.
+but potentially an unknown chain that depends on the user 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 agents, 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 agent, 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

@@ -1,202 +0,0 @@
-{
- "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

@@ -1,190 +0,0 @@
-{
- "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

@@ -1,242 +0,0 @@
-{
- "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

@@ -1,204 +0,0 @@
-{
- "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

@@ -1,228 +0,0 @@
-{
- "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

@@ -1,527 +0,0 @@
-{
- "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

@@ -1,892 +0,0 @@
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-873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
-874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

docs/modules/agents/agent_toolkits/vectorstore.ipynb

@@ -1,417 +0,0 @@
-{
- "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": [
-    "## Initialize 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 a response to the user.
+An action can either be using a tool and observing its output, or returning 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

@@ -1,494 +0,0 @@
-{
- "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

@@ -96,8 +96,12 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
       "Action: Search\n",
       "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
+      "Observation: \u001b[33;1m\u001b[1;3mDaniel Jason Sudeikis is an American actor, comedian, writer, and producer. In the 1990s, he began his career in improv comedy and performed with ComedySportz, iO Chicago, and The Second City.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' exact age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age exact\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mDaniel Jason Sudeikis. (1975-09-18) September 18, 1975 (age 47). Fairfax, Virginia, U.S. · Fort Scott Community College · Actor; comedian; producer; writer · 1997– ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now have the information I need to calculate the age raised to the 0.23 power\n",
       "Action: Calculator\n",
       "Action Input: 47^0.23\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
@@ -112,17 +116,18 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
       "Action: Search\n",
       "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Emilian Verstappen is a Belgian-Dutch racing driver and the 2021 and 2022 Formula One World Champion. He competes under the Dutch flag in Formula One with Red Bull Racing. Verstappen is the son of racing drivers Jos Verstappen, who also competed in Formula One, and Sophie Kumpen.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Emilian Verstappen's age.\n",
       "Action: Search\n",
-      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Action Input: \"Max Emilian Verstappen age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate 25 raised to the 0.23 power.\n",
       "Action: Calculator\n",
       "Action Input: 25^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.096651272316035\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Max Emilian Verstappen, who is 25 years old, won the most recent Formula 1 Grand Prix and his age raised to the 0.23 power is 2.096651272316035.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -135,14 +140,14 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
       "Action: Search\n",
       "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Vanessa Andreescu is a Canadian-Romanian professional tennis player. She has a career-high ranking of No. 4 in the world, and is the highest-ranked Canadian in the history of the Women's Tennis Association.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu.\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "Action Input: 19^0.34\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.7212987634680084\n",
       "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
+      "Final Answer: Bianca Andreescu, aged 19, won the US Open women's final in 2019. Her age raised to the 0.34 power is 2.7212987634680084.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -165,7 +170,7 @@
       "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Serial executed in 65.11 seconds.\n"
+      "Serial executed in 94.83 seconds.\n"
      ]
     }
    ],
@@ -212,91 +217,96 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "\u001b[33;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
       "Action: Search\n",
-      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
+      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\u001b[31;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
       "Action: Search\n",
-      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
+      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
-      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[38;5;200m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Emilian Verstappen is a Belgian-Dutch racing driver and the 2021 and 2022 Formula One World Champion. He competes under the Dutch flag in Formula One with Red Bull Racing. Verstappen is the son of racing drivers Jos Verstappen, who also competed in Formula One, and Sophie Kumpen.\u001b[0m\n",
+      "Thought:\n",
       "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
-      "Thought:\n",
       "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Thought:\u001b[31;1m\u001b[1;3m I need to find out Max Emilian Verstappen's age.\n",
       "Action: Search\n",
-      "Action Input: \"Jason Sudeikis age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
-      "Action: Search\n",
-      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
-      "Action: Search\n",
-      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
-      "Action: Search\n",
-      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Action Input: \"Max Emilian Verstappen age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
+      "Thought:\u001b[38;5;200m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
       "Action: Search\n",
       "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
-      "Action: Calculator\n",
-      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Vanessa Andreescu is a Canadian-Romanian professional tennis player. She has a career-high ranking of No. 4 in the world, and is the highest-ranked Canadian in the history of the Women's Tennis Association.\u001b[0m\n",
+      "Thought:\u001b[36;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
       "Action: Search\n",
-      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
-      "Action: Calculator\n",
-      "Action Input: 47^0.23\u001b[0m\n",
+      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mDaniel Jason Sudeikis is an American actor, comedian, writer, and producer. In the 1990s, he began his career in improv comedy and performed with ComedySportz, iO Chicago, and The Second City.\u001b[0m\n",
+      "Thought:\u001b[33;1m\u001b[1;3m I need to find out Jay-Z's age\n",
+      "Action: Search\n",
+      "Action Input: \"How old is Jay-Z?\"\u001b[0m\u001b[36;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
+      "Thought:\n",
+      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
+      "Thought:\u001b[38;5;200m\u001b[1;3m I now know the age of Bianca Andreescu.\n",
+      "Action: Calculator\n",
+      "Action Input: 19^0.34\u001b[0m\u001b[31;1m\u001b[1;3m I now need to calculate 25 raised to the 0.23 power.\n",
       "Action: Calculator\n",
       "Action Input: 25^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.7212987634680084\n",
       "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' exact age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age exact\"\u001b[0m\u001b[33;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
+      "Action Input: 53^0.19\u001b[0m\u001b[36;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
       "Action: Calculator\n",
       "Action Input: 36^0.334\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "Observation: \u001b[33;1m\u001b[1;3mDaniel Jason Sudeikis. (1975-09-18) September 18, 1975 (age 47). Fairfax, Virginia, U.S. · Fort Scott Community College · Actor; comedian; producer; writer · 1997– ...\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.096651272316035\n",
       "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now have the information I need to calculate the age raised to the 0.23 power\n",
+      "Action: Calculator\n",
+      "Action Input: 47^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bianca Andreescu, aged 19, won the US Open women's final in 2019. Her age raised to the 0.34 power is 2.7212987634680084.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
+      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Concurrent executed in 12.38 seconds.\n"
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Max Emilian Verstappen, who is 25 years old, won the most recent Formula 1 Grand Prix and his age raised to the 0.23 power is 2.096651272316035.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Concurrent executed in 25.06 seconds.\n"
      ]
     }
    ],
@@ -306,10 +316,12 @@
     "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
     "    # but you must manually close the client session at the end of your program/event loop\n",
     "    aiosession = ClientSession()\n",
-    "    for _ in questions:\n",
-    "        manager = CallbackManager([StdOutCallbackHandler()])\n",
+    "    colors = [\"blue\", \"green\", \"red\", \"pink\", \"yellow\"]\n",
+    "    for color in colors:\n",
+    "        # Use a custom CallbackManager to print in different colors.\n",
+    "        manager = CallbackManager([StdOutCallbackHandler(color=color)])\n",
     "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
-    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
+    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
     "        agents.append(\n",
     "            initialize_agent(async_tools, llm, agent=\"zero-shot-react-description\", verbose=True, callback_manager=manager)\n",
     "        )\n",

docs/modules/agents/examples/chat_conversation_agent.ipynb

@@ -1,309 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4658d71a",
-   "metadata": {},
-   "source": [
-    "# Conversation Agent (for Chat Models)\n",
-    "\n",
-    "This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.\n",
-    "\n",
-    "This is accomplished with a specific type of agent (`chat-conversational-react-description`) which expects to be used with a memory component."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f4f5d1a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f65308ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import Tool\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.utilities import SerpAPIWrapper\n",
-    "from langchain.agents import initialize_agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5fb14d6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = SerpAPIWrapper()\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Current Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\"\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "dddc34c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "cafe9bc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm=ChatOpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(tools, llm, agent=\"chat-conversational-react-description\", verbose=True, memory=memory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dc70b454",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Hello Bob! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"hi, i am bob\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3dcf7953",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Your name is Bob.\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Your name is Bob.'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"what's my name?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "aa05f566",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Current Search\",\n",
-      "    \"action_input\": \"Thai food dinner recipes\"\n",
-      "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c5d8b7ea",
-   "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```json\n",
-      "{\n",
-      "    \"action\": \"Current Search\",\n",
-      "    \"action_input\": \"who won the world cup in 1978\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m```json\n",
-      "{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f608889b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Current Search\",\n",
-      "    \"action_input\": \"weather in pomfret\"\n",
-      "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
-      "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"whats the weather like in pomfret?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0084efd6",
-   "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/custom_agent.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "id": "e21d2098",
    "metadata": {},
    "outputs": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -187,12 +187,16 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out how many people live in Canada\n",
       "Action: Search\n",
-      "Action Input: Population of Canada 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\u001b[0m\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the population of Canada\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the population of Canada\n",
+      "Final Answer: Arrr, Canada be home to over 37 million people!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -200,16 +204,16 @@
     {
      "data": {
       "text/plain": [
-       "\"Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\""
+       "'Arrr, Canada be home to over 37 million people!'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.run(\"How many people live in canada as of 2023?\")"
+    "agent_executor.run(\"How many people live in canada?\")"
    ]
   },
   {
@@ -223,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "id": "43dbfa2f",
    "metadata": {},
    "outputs": [],
@@ -244,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "id": "0f087313",
    "metadata": {},
    "outputs": [],
@@ -254,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "id": "92c75a10",
    "metadata": {},
    "outputs": [],
@@ -264,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 23,
    "id": "ac5b83bf",
    "metadata": {},
    "outputs": [],
@@ -274,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 24,
    "id": "c960e4ff",
    "metadata": {},
    "outputs": [
@@ -285,29 +289,56 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023.\n",
+      "\u001b[32;1m\u001b[1;3mThought: I should look up the population of Canada.\n",
       "Action: Search\n",
-      "Action Input: Population of Canada in 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look for the population of Canada.\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the population of Canada.\n",
+      "Final Answer: La popolazione del Canada è di circa 37 milioni di persone.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.'"
+       "'La popolazione del Canada è di circa 37 milioni di persone.'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.run(input=\"How many people live in canada as of 2023?\", language=\"italian\")"
+    "agent_executor.run(input=\"How many people live in canada?\", language=\"italian\")"
    ]
   },
   {
@@ -345,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/custom_tools.ipynb

@@ -7,27 +7,31 @@
    "source": [
     "# Defining Custom Tools\n",
     "\n",
-    "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
+    "When constructing your own agent, you will need to provide it with a list of Tools that it can use. A Tool is defined as below.\n",
     "\n",
-    "- name (str), is required\n",
-    "- description (str), is optional\n",
-    "- return_direct (bool), defaults to False\n",
+    "```python\n",
+    "@dataclass \n",
+    "class Tool:\n",
+    "    \"\"\"Interface for tools.\"\"\"\n",
     "\n",
-    "The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
+    "    name: str\n",
+    "    func: Callable[[str], str]\n",
+    "    description: Optional[str] = None\n",
+    "    return_direct: bool = True\n",
+    "```\n",
     "\n",
-    "There are two ways to define a tool, we will cover both in the example below."
+    "The two required components of a Tool are the name and then the tool itself. A tool description is optional, as it is needed for some agents but not all. You can create these tools directly, but we also provide a decorator to easily convert any function into a tool."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "1aaba18c",
    "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"
    ]
@@ -42,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "36ed392e",
    "metadata": {},
    "outputs": [],
@@ -55,18 +59,8 @@
    "id": "f8bc72c2",
    "metadata": {},
    "source": [
-    "## Completely New Tools \n",
-    "First, we show how to create completely new tools from scratch.\n",
-    "\n",
-    "There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b63fcc3b",
-   "metadata": {},
-   "source": [
-    "### Tool dataclass"
+    "## Completely New Tools\n",
+    "First, we show how to create completely new tools from scratch."
    ]
   },
   {
@@ -95,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "5b93047d",
    "metadata": {},
    "outputs": [],
@@ -107,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "6f96a891",
    "metadata": {},
    "outputs": [
@@ -118,161 +112,45 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
+      "Action Input: Olivia Wilde's boyfriend\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mHarry Styles\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate Harry Styles' age raised to the 0.23 power.\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
+      "Action Input: 23^0.23\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
+      "23^0.23\u001b[32;1m\u001b[1;3m\n",
       "```python\n",
       "import math\n",
-      "print(math.pow(22, 0.43))\n",
+      "print(math.pow(23, 0.23))\n",
       "```\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.0568252837687546\n",
       "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.0568252837687546\n",
       "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Harry Styles' age raised to the 0.23 power is 2.0568252837687546.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
+       "\"Harry Styles' age raised to the 0.23 power is 2.0568252837687546.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f12eaf0",
-   "metadata": {},
-   "source": [
-    "### Subclassing the BaseTool class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c58a7c40",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class CustomSearchTool(BaseTool):\n",
-    "    name = \"Search\"\n",
-    "    description = \"useful for when you need to answer questions about current events\"\n",
-    "\n",
-    "    def _run(self, query: str) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        return search.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"BingSearchRun does not support async\")\n",
-    "    \n",
-    "class CustomCalculatorTool(BaseTool):\n",
-    "    name = \"Calculator\"\n",
-    "    description = \"useful for when you need to answer questions about math\"\n",
-    "\n",
-    "    def _run(self, query: str) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        return llm_math_chain.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"BingSearchRun does not support async\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3318a46f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = [CustomSearchTool(), CustomCalculatorTool()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ee2d0f3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6a2cebbf",
-   "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 who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
-      "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22, 0.43))\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
   {
@@ -287,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "8f15307d",
    "metadata": {},
    "outputs": [],
@@ -302,17 +180,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "0a23b91b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8700>, coroutine=None)"
+       "Tool(name='search_api', func=<function search_api at 0x10dad7d90>, description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -331,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "28cdf04d",
    "metadata": {},
    "outputs": [],
@@ -344,17 +222,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "1085a4bd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8670>, coroutine=None)"
+       "Tool(name='search', func=<function search_api at 0x112301bd0>, description='search(query: str) -> str - Searches the API for the query.', return_direct=True)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,29 +304,28 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Google Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mHarry Styles\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age\n",
       "Action: Google Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
+      "Action Input: \"Harry Styles age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "Action Input: 28^0.23\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
       "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
+       "\"Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.1520202182226886.\""
       ]
      },
      "execution_count": 12,
@@ -457,7 +334,7 @@
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
   {
@@ -477,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -505,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "4b9a7849",
    "metadata": {},
    "outputs": [
@@ -532,7 +409,7 @@
        "\"'All I Want For Christmas Is You' by Mariah Carey.\""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "3bb6185f",
    "metadata": {},
    "outputs": [],
@@ -570,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "id": "113ddb84",
    "metadata": {},
    "outputs": [],
@@ -581,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "id": "582439a6",
    "metadata": {},
    "outputs": [
@@ -607,7 +484,7 @@
        "'Answer: 1.2599210498948732'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -641,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/human_tools.ipynb

@@ -1,132 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Human as a tool\n",
-    "\n",
-    "Human are AGI so they can certainly be used as a tool to help out AI agent \n",
-    "when it is confused."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.agents import load_tools, initialize_agent\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0.0)\n",
-    "math_llm = OpenAI(temperature=0.0)\n",
-    "tools = load_tools(\n",
-    "    [\"human\", \"llm-math\"], \n",
-    "    llm=math_llm,\n",
-    ")\n",
-    "\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the above code you can see the tool takes input directly from command line.\n",
-    "You can customize `prompt_func` and `input_func` according to your need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
-      "Action: Human\n",
-      "Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
-      "\n",
-      "Do you know when Eric Zhu's birthday is?\n",
-      "last week\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
-      "Action: Human\n",
-      "Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
-      "\n",
-      "Do you know the specific date of Eric Zhu's birthday?\n",
-      "august 1st\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
-      "Action: Calculator\n",
-      "Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
-      "Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
-    "# Answer with \"last week\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/modules/agents/examples/intermediate_steps.ipynb

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "36ed392e",
    "metadata": {},
    "outputs": [],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6abf3b08",
    "metadata": {},
    "outputs": [],
@@ -72,28 +72,23 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I should look up who Leo DiCaprio is dating\n",
+      "\u001b[32;1m\u001b[1;3m I should look up Olivia Wilde's boyfriend's age\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I should look up how old Camila Morrone is\n",
-      "Action: Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I should calculate what 25 years raised to the 0.43 power is\n",
+      "Action Input: \"Olivia Wilde's boyfriend's age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should use the calculator to raise that number to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "Action Input: 28^0.23\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
       "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and she is 3.991298452658078 years old.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Final Answer: 2.1520202182226886\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "response = agent({\"input\":\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"})"
+    "response = agent({\"input\":\"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\"})"
    ]
   },
   {
@@ -106,7 +101,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(AgentAction(tool='Search', tool_input='Leo DiCaprio girlfriend', log=' I should look up who Leo DiCaprio is dating\\nAction: Search\\nAction Input: \"Leo DiCaprio girlfriend\"'), 'Camila Morrone'), (AgentAction(tool='Search', tool_input='Camila Morrone age', log=' I should look up how old Camila Morrone is\\nAction: Search\\nAction Input: \"Camila Morrone age\"'), '25 years'), (AgentAction(tool='Calculator', tool_input='25^0.43', log=' I should calculate what 25 years raised to the 0.43 power is\\nAction: Calculator\\nAction Input: 25^0.43'), 'Answer: 3.991298452658078\\n')]\n"
+      "[(AgentAction(tool='Search', tool_input=\"Olivia Wilde's boyfriend's age\", log=' I should look up Olivia Wilde\\'s boyfriend\\'s age\\nAction: Search\\nAction Input: \"Olivia Wilde\\'s boyfriend\\'s age\"'), '28 years'), (AgentAction(tool='Calculator', tool_input='28^0.23', log=' I should use the calculator to raise that number to the 0.23 power\\nAction: Calculator\\nAction Input: 28^0.23'), 'Answer: 2.1520202182226886\\n')]\n"
      ]
     }
    ],
@@ -129,26 +124,18 @@
       "  [\n",
       "    [\n",
       "      \"Search\",\n",
-      "      \"Leo DiCaprio girlfriend\",\n",
-      "      \" I should look up who Leo DiCaprio is dating\\nAction: Search\\nAction Input: \\\"Leo DiCaprio girlfriend\\\"\"\n",
+      "      \"Olivia Wilde's boyfriend's age\",\n",
+      "      \" I should look up Olivia Wilde's boyfriend's age\\nAction: Search\\nAction Input: \\\"Olivia Wilde's boyfriend's age\\\"\"\n",
       "    ],\n",
-      "    \"Camila Morrone\"\n",
-      "  ],\n",
-      "  [\n",
-      "    [\n",
-      "      \"Search\",\n",
-      "      \"Camila Morrone age\",\n",
-      "      \" I should look up how old Camila Morrone is\\nAction: Search\\nAction Input: \\\"Camila Morrone age\\\"\"\n",
-      "    ],\n",
-      "    \"25 years\"\n",
+      "    \"28 years\"\n",
       "  ],\n",
       "  [\n",
       "    [\n",
       "      \"Calculator\",\n",
-      "      \"25^0.43\",\n",
-      "      \" I should calculate what 25 years raised to the 0.43 power is\\nAction: Calculator\\nAction Input: 25^0.43\"\n",
+      "      \"28^0.23\",\n",
+      "      \" I should use the calculator to raise that number to the 0.23 power\\nAction: Calculator\\nAction Input: 28^0.23\"\n",
       "    ],\n",
-      "    \"Answer: 3.991298452658078\\n\"\n",
+      "    \"Answer: 2.1520202182226886\\n\"\n",
       "  ]\n",
       "]\n"
      ]
@@ -178,7 +165,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.0 64-bit ('llm-env')",
    "language": "python",
    "name": "python3"
   },
@@ -192,7 +179,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.0"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/load_from_hub.ipynb

@@ -12,33 +12,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "bd4450a2",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No `_type` key found, defaulting to `prompt`.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "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"
      ]
     },
     {
@@ -47,7 +40,7 @@
        "'Manacor, Mallorca, Spain.'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,8 +54,7 @@
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        func=search.run\n",
     "    )\n",
     "]\n",
     "\n",
@@ -71,6 +63,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3aede965",
    "metadata": {},
@@ -82,29 +75,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e679f7b6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No `_type` key found, defaulting to `prompt`.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d3d6697",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -123,7 +100,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/examples/max_iterations.ipynb

@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47653ac6",
+   "id": "ebde3ea6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "fca094af",
    "metadata": {},
    "outputs": [],
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "0fd3ef0a",
    "metadata": {},
    "outputs": [
@@ -123,14 +123,13 @@
       "\u001b[32;1m\u001b[1;3m I need to use the Jester tool\n",
       "Action: Jester\n",
       "Action Input: foo\u001b[0m\n",
-      "Observation: foo is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3m I should try Jester again\n",
+      "Observation: Jester is not a valid tool, try another one.\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should try again\n",
       "Action: Jester\n",
       "Action Input: foo\u001b[0m\n",
-      "Observation: foo is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Observation: Jester is not a valid tool, try another one.\n",
+      "Thought:\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
@@ -139,7 +138,7 @@
        "'Agent stopped due to max iterations.'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "3cc521bb",
    "metadata": {},
    "outputs": [],
@@ -168,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "1618d316",
    "metadata": {},
    "outputs": [
@@ -182,24 +181,22 @@
       "\u001b[32;1m\u001b[1;3m I need to use the Jester tool\n",
       "Action: Jester\n",
       "Action Input: foo\u001b[0m\n",
-      "Observation: foo is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3m I should try Jester again\n",
+      "Observation: Jester is not a valid tool, try another one.\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should try again\n",
       "Action: Jester\n",
       "Action Input: foo\u001b[0m\n",
-      "Observation: foo is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Final Answer: Jester is the tool to use for this question.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Observation: Jester is not a valid tool, try another one.\n",
+      "Thought:\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Jester is the tool to use for this question.'"
+       "'Jester is not a valid tool, try another one.'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/examples/multi_input_tool.ipynb

@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "6db1d43f",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "aa25d0ca",
    "metadata": {},
    "outputs": [
@@ -85,8 +85,7 @@
       "Observation: \u001b[36;1m\u001b[1;3m12\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: 3 times 4 is 12\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
@@ -95,7 +94,7 @@
        "'3 times 4 is 12'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,7 +114,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.0 64-bit ('llm-env')",
    "language": "python",
    "name": "python3"
   },
@@ -129,7 +128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.0"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/search_tools.ipynb

@@ -12,13 +12,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "e6860c2d",
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.agents import load_tools\n",
@@ -38,86 +34,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee251155",
-   "metadata": {},
-   "source": [
-    "## Google Serper API Wrapper\n",
-    "\n",
-    "First, let's try to use the Google Serper API tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0cdaa487",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = load_tools([\"google-serper\"], llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "01b1ab4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5cf44ec0",
-   "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 should look up the current weather conditions.\n",
-      "Action: Search\n",
-      "Action Input: \"weather in Pomfret\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m37°F\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the current temperature in Pomfret.\n",
-      "Final Answer: The current temperature in Pomfret is 37°F.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The current temperature in Pomfret is 37°F.'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"What is the weather in Pomfret?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e39fc46",
+   "id": "a09ca013",
    "metadata": {},
    "source": [
     "## SerpAPI\n",
     "\n",
-    "Now, let's use the SerpAPI tool."
+    "First, let's use the SerpAPI tool."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "e1c39a0f",
+   "execution_count": 4,
+   "id": "dd4ce6d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,8 +54,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "900dd6cb",
+   "execution_count": 5,
+   "id": "ef63bb84",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,8 +64,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "342ee8ec",
+   "execution_count": 6,
+   "id": "53e24f5d",
    "metadata": {},
    "outputs": [
     {
@@ -150,20 +78,19 @@
       "\u001b[32;1m\u001b[1;3m I need to find out what the current weather is in Pomfret.\n",
       "Action: Search\n",
       "Action Input: \"weather in Pomfret\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mPartly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ...\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mShowers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the current weather in Pomfret.\n",
-      "Final Answer: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Final Answer: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.'"
+       "'Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adc8bb68",
+   "id": "8ef49137",
    "metadata": {},
    "source": [
     "## GoogleSearchAPIWrapper\n",
@@ -185,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ef24f92d",
+   "id": "3e9c7c20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +122,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "909cd28b",
+   "id": "b83624dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "46515d2a",
+   "id": "9d5835e2",
    "metadata": {},
    "outputs": [
     {
@@ -238,97 +165,11 @@
    "source": [
     "agent.run(\"What is the weather in Pomfret?\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eabad3af",
-   "metadata": {},
-   "source": [
-    "## SearxNG Meta Search Engine\n",
-    "\n",
-    "Here we will be using a self hosted SearxNG meta search engine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b196c704",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = load_tools([\"searx-search\"], searx_host=\"http://localhost:8888\", llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9023eeaa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3aad92c1",
-   "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 should look up the current weather\n",
-      "Action: SearX Search\n",
-      "Action Input: \"weather in Pomfret\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch.\n",
-      "\n",
-      "10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%....\n",
-      "\n",
-      "10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F....\n",
-      "\n",
-      "Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo.\n",
-      "\n",
-      "Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu When It’s Cold Coast-To-Coast...\n",
-      "\n",
-      "Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast...\n",
-      "\n",
-      "Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast...\n",
-      "\n",
-      "Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast...\n",
-      "\n",
-      "12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ...\n",
-      "\n",
-      "Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"What is the weather in Pomfret\")"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.0 64-bit ('llm-env')",
    "language": "python",
    "name": "python3"
   },
@@ -342,7 +183,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.9.0"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/serialization.ipynb

@@ -67,9 +67,7 @@
       "            ],\r\n",
       "            \"output_parser\": null,\r\n",
       "            \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
-      "            \"template_format\": \"f-string\",\r\n",
-      "            \"validate_template\": true,\r\n",
-      "            \"_type\": \"prompt\"\r\n",
+      "            \"template_format\": \"f-string\"\r\n",
       "        },\r\n",
       "        \"llm\": {\r\n",
       "            \"model_name\": \"text-davinci-003\",\r\n",
@@ -87,10 +85,6 @@
       "        \"output_key\": \"text\",\r\n",
       "        \"_type\": \"llm_chain\"\r\n",
       "    },\r\n",
-      "    \"allowed_tools\": [\r\n",
-      "        \"Search\",\r\n",
-      "        \"Calculator\"\r\n",
-      "    ],\r\n",
       "    \"return_values\": [\r\n",
       "        \"output\"\r\n",
       "    ],\r\n",
@@ -113,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "eb660b76",
    "metadata": {},
    "outputs": [],
@@ -146,7 +140,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/examples/sharedmemory_for_tools.ipynb

@@ -1,552 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "fa6802ac",
-   "metadata": {},
-   "source": [
-    "# Adding SharedMemory to an Agent and its Tools\n",
-    "\n",
-    "This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n",
-    "\n",
-    "- [Adding memory to an LLM Chain](../../memory/examples/adding_memory.ipynb)\n",
-    "- [Custom Agents](custom_agent.ipynb)\n",
-    "\n",
-    "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. And, the summarization tool also needs access to the conversation memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8db95912",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
-    "from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
-    "from langchain import OpenAI, LLMChain, PromptTemplate\n",
-    "from langchain.utilities import GoogleSearchAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "06b7187b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"This is a conversation between a human and a bot:\n",
-    "\n",
-    "{chat_history}\n",
-    "\n",
-    "Write a summary of the conversation for {input}:\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"input\", \"chat_history\"], \n",
-    "    template=template\n",
-    ")\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
-    "readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
-    "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(), \n",
-    "    prompt=prompt, \n",
-    "    verbose=True, \n",
-    "    memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "97ad8467",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = GoogleSearchAPIWrapper()\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name = \"Summary\",\n",
-    "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
-    "    )\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e3439cd6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
-    "suffix = \"\"\"Begin!\"\n",
-    "\n",
-    "{chat_history}\n",
-    "Question: {input}\n",
-    "{agent_scratchpad}\"\"\"\n",
-    "\n",
-    "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0021675b",
-   "metadata": {},
-   "source": [
-    "We can now construct the LLMChain, with the Memory object, and then create the agent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c56a0e73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
-    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ca4bc1fb",
-   "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 should research ChatGPT to answer this question.\n",
-      "Action: Search\n",
-      "Action Input: \"ChatGPT\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"What is ChatGPT?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45627664",
-   "metadata": {},
-   "source": [
-    "To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "eecc0462",
-   "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 find out who developed ChatGPT\n",
-      "Action: Search\n",
-      "Action Input: Who developed ChatGPT\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'ChatGPT was developed by OpenAI.'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"Who developed it?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c34424cf",
-   "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 simplify the conversation for a 5 year old.\n",
-      "Action: Summary\n",
-      "Action Input: My daughter 5 years old\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
-      "\n",
-      "Human: What is ChatGPT?\n",
-      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
-      "Human: Who developed it?\n",
-      "AI: ChatGPT was developed by OpenAI.\n",
-      "\n",
-      "Write a summary of the conversation for My daughter 5 years old:\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3m\n",
-      "The conversation was about ChatGPT, an artificial intelligence chatbot. It was created by OpenAI and can send and receive images while chatting.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "4ebd8326",
-   "metadata": {},
-   "source": [
-    "Confirm that the memory was correctly updated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b91f8c85",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Human: What is ChatGPT?\n",
-      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
-      "Human: Who developed it?\n",
-      "AI: ChatGPT was developed by OpenAI.\n",
-      "Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
-      "AI: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(agent_chain.memory.buffer)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cc3d0aa4",
-   "metadata": {},
-   "source": [
-    "For comparison, below is a bad example that uses the same memory for both the Agent and the tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3359d043",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## This is a bad practice for using the memory.\n",
-    "## Use the ReadOnlySharedMemory class, as shown above.\n",
-    "\n",
-    "template = \"\"\"This is a conversation between a human and a bot:\n",
-    "\n",
-    "{chat_history}\n",
-    "\n",
-    "Write a summary of the conversation for {input}:\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"input\", \"chat_history\"], \n",
-    "    template=template\n",
-    ")\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
-    "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(), \n",
-    "    prompt=prompt, \n",
-    "    verbose=True, \n",
-    "    memory=memory,  # <--- this is the only change\n",
-    ")\n",
-    "\n",
-    "search = GoogleSearchAPIWrapper()\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name = \"Summary\",\n",
-    "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
-    "suffix = \"\"\"Begin!\"\n",
-    "\n",
-    "{chat_history}\n",
-    "Question: {input}\n",
-    "{agent_scratchpad}\"\"\"\n",
-    "\n",
-    "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
-    ")\n",
-    "\n",
-    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
-    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "970d23df",
-   "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 should research ChatGPT to answer this question.\n",
-      "Action: Search\n",
-      "Action Input: \"ChatGPT\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"What is ChatGPT?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d9ea82f0",
-   "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 find out who developed ChatGPT\n",
-      "Action: Search\n",
-      "Action Input: Who developed ChatGPT\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'ChatGPT was developed by OpenAI.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"Who developed it?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5b1f9223",
-   "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 simplify the conversation for a 5 year old.\n",
-      "Action: Summary\n",
-      "Action Input: My daughter 5 years old\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
-      "\n",
-      "Human: What is ChatGPT?\n",
-      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
-      "Human: Who developed it?\n",
-      "AI: ChatGPT was developed by OpenAI.\n",
-      "\n",
-      "Write a summary of the conversation for My daughter 5 years old:\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3m\n",
-      "The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "d07415da",
-   "metadata": {},
-   "source": [
-    "The final answer is not wrong, but we see the 3rd Human input is actually from the agent in the memory because the memory was modified by the summary tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "32f97b21",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Human: What is ChatGPT?\n",
-      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
-      "Human: Who developed it?\n",
-      "AI: ChatGPT was developed by OpenAI.\n",
-      "Human: My daughter 5 years old\n",
-      "AI: \n",
-      "The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\n",
-      "Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
-      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(agent_chain.memory.buffer)"
-   ]
-  }
- ],
- "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/getting_started.ipynb

@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "03208e2b",
    "metadata": {},
    "outputs": [],
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "244ee75c",
    "metadata": {},
    "outputs": [
@@ -116,47 +116,38 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mHarry Styles\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age\n",
       "Action: Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
+      "Action Input: \"Harry Styles age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "Action Input: 28^0.23\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
       "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
-      "\n",
+      "Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
+       "\"Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.1520202182226886.\""
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5901695b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -175,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/how_to_guides.rst

@@ -1,23 +1,12 @@
 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).
@@ -30,48 +19,7 @@ The first category of how-to guides here cover specific parts of working with ag
 
 `Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.
 
-
-.. 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.
+The next set of examples are all end-to-end agents for specific applications.
 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>`_
@@ -101,13 +49,16 @@ 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

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -84,55 +84,54 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
-      "Action Input: \"Who is Leo DiCaprio's girlfriend?\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
+      "Action Input: \"Who is Olivia Wilde's boyfriend?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mHarry Styles\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age\n",
       "Action: Search\n",
-      "Action Input: \"How old is Camila Morrone?\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
+      "Action Input: \"How old is Harry Styles?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
+      "Action Input: 28^0.23\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "25^0.43\u001b[32;1m\u001b[1;3m\n",
+      "28^0.23\u001b[32;1m\u001b[1;3m\n",
       "```python\n",
       "import math\n",
-      "print(math.pow(25, 0.43))\n",
+      "print(math.pow(28, 0.23))\n",
       "```\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
       "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
       "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Final Answer: Harry Styles is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.'"
+       "'Harry Styles is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "mrkl.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -146,32 +145,31 @@
       "\u001b[32;1m\u001b[1;3m I need to find out the artist's full name and then search the FooBar database for their albums.\n",
       "Action: Search\n",
       "Action Input: \"The Storm Before the Calm\" artist\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette - the storm before the calm - Amazon.com Music.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums.\n",
       "Action: FooBar DB\n",
-      "Action Input: What albums by Alanis Morissette are in the FooBar database?\u001b[0m\n",
+      "Action Input: What albums of Alanis Morissette are in the FooBar database?\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums by Alanis Morissette are in the FooBar database? \n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Title FROM Album INNER JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alanis Morissette' LIMIT 5;\u001b[0m\n",
+      "What albums of Alanis Morissette are in the FooBar database? \n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette');\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m The album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\u001b[0m\n",
+      "\u001b[1m> Finished SQLDatabaseChain chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Observation: \u001b[38;5;200m\u001b[1;3m The album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Alanis Morissette is the artist who recently released an album called 'The Storm Before the Calm' and the album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.'"
+       "\"Alanis Morissette is the artist who recently released an album called 'The Storm Before the Calm' and the album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +203,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/implementations/mrkl_chat.ipynb

@@ -1,253 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f1390152",
-   "metadata": {},
-   "source": [
-    "# MRKL Chat\n",
-    "\n",
-    "This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39ea3638",
-   "metadata": {},
-   "source": [
-    "This uses the example Chinook database.\n",
-    "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": "code",
-   "execution_count": 1,
-   "id": "ac561cc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.chat_models import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "07e96d99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0)\n",
-    "llm1 = OpenAI(temperature=0)\n",
-    "search = SerpAPIWrapper()\n",
-    "llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
-    "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
-    "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"FooBar DB\",\n",
-    "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
-    "    )\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a069c4b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mrkl = initialize_agent(tools, llm, agent=\"chat-zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e603cd7d",
-   "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: The first question requires a search, while the second question requires a calculator.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"22.0^(0.43)\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22.0, 0.43))\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Camila Morrone, 3.777824273683966.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a5c07010",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
-      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who recently released an album called 'The Storm Before the Calm'\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"FooBar DB\",\n",
-      "  \"action_input\": \"What albums does Alanis Morissette have in the database?\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums does Alanis Morissette have in the database? \n",
-      "SQLQuery:"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: 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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
-      "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
-      "Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af016a70",
-   "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/implementations/natbot.py

@@ -33,6 +33,7 @@ def run_cmd(cmd: str, _crawler: Crawler) -> None:
 
 
 if __name__ == "__main__":
+
     objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
     print("\nWelcome to natbot! What is your objective?")
     i = input()

docs/modules/agents/implementations/react.ipynb

@@ -24,13 +24,11 @@
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Search\",\n",
-    "        func=docstore.search,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        func=docstore.search\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Lookup\",\n",
-    "        func=docstore.lookup,\n",
-    "        description=\"useful for when you need to ask with lookup\"\n",
+    "        func=docstore.lookup\n",
     "    )\n",
     "]\n",
     "\n",
@@ -83,7 +81,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.0 64-bit ('llm-env')",
    "language": "python",
    "name": "python3"
   },
@@ -97,7 +95,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.0"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/implementations/self_ask_with_search.ipynb

@@ -52,8 +52,7 @@
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        func=search.run\n",
     "    )\n",
     "]\n",
     "\n",
@@ -64,7 +63,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.0 64-bit ('llm-env')",
    "language": "python",
    "name": "python3"
   },
@@ -78,7 +77,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.0"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/key_concepts.md

@@ -8,9 +8,3 @@ 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.
-For more detailed information on toolkits and their use cases, see [this documentation](how_to_guides.rst#agent-toolkits) (the "Agent Toolkits" section).

docs/modules/agents/tools.md

@@ -119,36 +119,3 @@ Below is a list of all supported tools and relevant information:
 - Requires LLM: No
 - Extra Parameters: `google_api_key`, `google_cse_id`
 - For more information on this, see [this page](../../ecosystem/google_search.md)
-
-**searx-search**
-
-- Tool Name: Search
-- Tool Description: A wrapper around SearxNG meta search engine. Input should be a search query. 
-- Notes: SearxNG is easy to deploy self-hosted. It is a good privacy friendly alternative to Google Search. Uses the SearxNG API. 
-- Requires LLM: No
-- Extra Parameters: `searx_host`
-
-**google-serper**
-
-- Tool Name: Search
-- Tool Description: A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.
-- Notes: Calls the [serper.dev](https://serper.dev) Google Search API and then parses results.
-- Requires LLM: No
-- Extra Parameters: `serper_api_key`
-- For more information on this, see [this page](../../ecosystem/google_serper.md)
-
-**wikipedia**
-
-- Tool Name: Wikipedia
-- Tool Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
-- Notes: Uses the [wikipedia](https://pypi.org/project/wikipedia/) Python package to call the MediaWiki API and then parses results.
-- Requires LLM: No
-- Extra Parameters: `top_k_results`
-
-**podcast-api**
-
-- Tool Name: Podcast API
-- Tool Description: Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.
-- Notes: A natural language connection to the Listen Notes Podcast API (`https://www.PodcastAPI.com`), specifically the `/search/` endpoint.
-- Requires LLM: Yes
-- Extra Parameters: `listen_api_key` (your api key to access this endpoint)

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 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.
+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.
 
 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

@@ -9,7 +9,7 @@
     "\n",
     "LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",
-    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](../indexes/chain_examples/question_answering.html). Async support for other chains is on the roadmap."
+    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`). Async support for other chains is on the roadmap."
    ]
   },
   {
@@ -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"
      ]
     }
    ],
@@ -124,7 +124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/combine_docs.md

@@ -19,20 +19,20 @@ to pass to the language model. This is implemented in LangChain as the `StuffDoc
 
 **Cons:** Most LLMs have a context length, and for large documents (or many documents) this will not work as it will result in a prompt larger than the context length.
 
-The main downside of this method is that it only works on smaller pieces of data. Once you are working
+The main downside of this method is that it only works one smaller pieces of data. Once you are working
 with many pieces of data, this approach is no longer feasible. The next two approaches are designed to help deal with that.
 
 ## Map Reduce
-This method involves running an initial prompt on each chunk of data (for summarization tasks, this 
+This method involves an initial prompt on each chunk of data (for summarization tasks, this 
 could be a summary of that chunk; for question-answering tasks, it could be an answer based solely on that chunk).
 Then a different prompt is run to combine all the initial outputs. This is implemented in the LangChain as the `MapReduceDocumentsChain`.
 
 **Pros:** Can scale to larger documents (and more documents) than `StuffDocumentsChain`. The calls to the LLM on individual documents are independent and can therefore be parallelized.
 
-**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. Loses some information during the final combined call.
+**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. Loses some information during the final combining call.
 
 ## Refine
-This method involves running an initial prompt on the first chunk of data, generating some output.
+This method involves an initial prompt on the first chunk of data, generating some output.
 For the remaining documents, that output is passed in, along with the next document, 
 asking the LLM to refine the output based on the new document. 
 
@@ -46,6 +46,6 @@ This method involves running an initial prompt on each chunk of data, that not o
 task but also gives a score for how certain it is in its answer. The responses are then
 ranked according to this score, and the highest score is returned.
 
-**Pros:** Similar pros as `MapReduceDocumentsChain`. Requires fewer calls, compared to `MapReduceDocumentsChain`.
+**Pros:** Similar pros as `MapReduceDocumentsChain`. Compared to `MapReduceDocumentsChain`, it requires fewer calls.
 
 **Cons:** Cannot combine information between documents. This means it is most useful when you expect there to be a single simple answer in a single document.

docs/modules/chains/combine_docs_examples/chat_vector_db.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "134a0785",
+   "metadata": {},
+   "source": [
+    "# Chat Vector DB\n",
+    "\n",
+    "This notebook goes over how to set up a chain to chat with a vector database. The only difference because this chain and the [VectorDBQAChain](./vector_db_qa.ipynb) is that this allows for passing in of a chat history which can be used to allow for follow up questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70c4e529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import ChatVectorDBChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a8930cf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "vectorstore = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7b4110f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3872432d",
+   "metadata": {},
+   "source": [
+    "Here's an example of asking a question with no chat history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7fe3e730",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bfff9cc8",
+   "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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e46edf7",
+   "metadata": {},
+   "source": [
+    "Here's an example of asking a question with some chat history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "00b4cf00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat_history = [(query, result[\"answer\"])]\n",
+    "query = \"Did he mention who she suceeded\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f01828d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Justice Stephen Breyer'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0f869c6",
+   "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/chains/combine_docs_examples/qa_with_sources.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "78f28130",
    "metadata": {},
    "outputs": [],
@@ -30,14 +30,14 @@
     "from langchain.embeddings.cohere import CohereEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
     "from langchain.docstore.document import Document\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "4da195a3",
    "metadata": {},
    "outputs": [],
@@ -52,26 +52,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "5ec2b55b",
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))])"
+    "docsearch = FAISS.from_texts(texts, embeddings, metadatas=[{\"source\": i} for i in range(len(texts))])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "5286f58f",
    "metadata": {},
    "outputs": [],
@@ -82,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "005a47e9",
    "metadata": {},
    "outputs": [],
@@ -102,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "3722373b",
    "metadata": {},
    "outputs": [
@@ -112,7 +103,7 @@
        "{'output_text': ' The president thanked Justice Breyer for his service.\\nSOURCES: 30-pl'}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,20 +356,6 @@
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d943c6c1",
-   "metadata": {},
-   "source": [
-    "**Batch Size**\n",
-    "\n",
-    "When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:\n",
-    "\n",
-    "```python\n",
-    "llm = OpenAI(batch_size=5, temperature=0)\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5bf0e1ab",
@@ -635,7 +612,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.output_parsers import RegexParser\n",
+    "from langchain.prompts.base import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",
@@ -722,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/combine_docs_examples/question_answering.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
+    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chaings: `stuff`, `map_reduce`, `refine`, `map-rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
    ]
   },
   {
@@ -28,41 +28,34 @@
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
     "from langchain.docstore.document import Document\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.indexes.vectorstore import VectorstoreIndexCreator"
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ef9305cc",
+   "id": "291f0117",
    "metadata": {},
    "outputs": [],
    "source": [
-    "index_creator = VectorstoreIndexCreator()"
+    "with open('../../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "291f0117",
+   "id": "fd9666a9",
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../state_of_the_union.txt')\n",
-    "docsearch = index_creator.from_loaders([loader])"
+    "docsearch = FAISS.from_texts(texts, embeddings)"
    ]
   },
   {
@@ -353,20 +346,6 @@
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6391b7ab",
-   "metadata": {},
-   "source": [
-    "**Batch Size**\n",
-    "\n",
-    "When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:\n",
-    "\n",
-    "```python\n",
-    "llm = OpenAI(batch_size=5, temperature=0)\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "6ea50ad0",
@@ -635,7 +614,7 @@
     }
    ],
    "source": [
-    "from langchain.output_parsers import RegexParser\n",
+    "from langchain.prompts.base import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",
@@ -694,7 +673,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/combine_docs_examples/summarize.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "e9db25f3",
    "metadata": {},
    "outputs": [],
@@ -81,17 +81,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "5cfa89b2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'"
+       "\" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and investing in American products to create jobs. President Biden and Vice President Harris have passed the American Rescue Plan and the Bipartisan Infrastructure Law to help working people and rebuild America.\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,7 +470,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/combine_docs_examples/vector_db_qa.ipynb

@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain import OpenAI, VectorDBQA"
    ]
@@ -28,25 +28,15 @@
    "execution_count": 2,
    "id": "5c7049db",
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
+    "with open('../../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
-    "docsearch = Chroma.from_documents(texts, embeddings)"
+    "docsearch = FAISS.from_texts(texts, embeddings)"
    ]
   },
   {
@@ -68,7 +58,7 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
      "execution_count": 4,
@@ -166,67 +156,6 @@
     "qa.run(query)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "90c7899a",
-   "metadata": {},
-   "source": [
-    "## Custom Prompts\n",
-    "You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](./question_answering.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a45232a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
-    "\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "Answer in Italian:\"\"\"\n",
-    "PROMPT = PromptTemplate(\n",
-    "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b5c8d1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain_type_kwargs = {\"prompt\": PROMPT}\n",
-    "qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", vectorstore=docsearch, chain_type_kwargs=chain_type_kwargs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "26ee7671",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" Il Presidente ha detto che Ketanji Brown Jackson è uno dei pensatori legali più importanti del nostro Paese, che continuerà l'eccellente eredità di giustizia Breyer. È un ex principale litigante in pratica privata, un ex difensore federale pubblico e appartiene a una famiglia di insegnanti e poliziotti delle scuole pubbliche. È un costruttore di consenso che ha ricevuto un ampio supporto da parte di Fraternal Order of Police e giudici designati da democratici e repubblicani.\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "qa.run(query)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0b8c37f7",
@@ -327,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/combine_docs_examples/vector_db_qa_with_sources.ipynb

@@ -21,7 +21,7 @@
     "from langchain.embeddings.cohere import CohereEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
-    "from langchain.vectorstores import Chroma"
+    "from langchain.vectorstores.faiss import FAISS"
    ]
   },
   {
@@ -41,27 +41,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "0e745d99",
    "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",
-      "Exiting: Cleaning up .chroma directory\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])"
+    "docsearch = FAISS.from_texts(texts, embeddings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
+   "id": "f42d79dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add in a fake source information\n",
+    "for i, d in enumerate(docsearch.docstore._dict.values()):\n",
+    "    d.metadata = {'source': f\"{i}-pl\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "8aa571ae",
    "metadata": {},
    "outputs": [],
@@ -71,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "aa859d4c",
    "metadata": {},
    "outputs": [],
@@ -83,18 +85,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "8ba36fa7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'answer': ' The president thanked Justice Breyer for his service and mentioned his legacy of excellence.\\n',\n",
+       "{'answer': ' The president thanked Justice Breyer for his service.\\n',\n",
        " 'sources': '30-pl'}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +165,7 @@
    "source": [
     "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
     "qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
-    "qa = VectorDBQAWithSourcesChain(combine_documents_chain=qa_chain, vectorstore=docsearch)"
+    "qa = VectorDBQAWithSourcesChain(combine_document_chain=qa_chain, vectorstore=docsearch)"
    ]
   },
   {
@@ -205,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/combine_docs_examples/vector_db_text_generation.ipynb

@@ -28,7 +28,7 @@
     "from langchain.docstore.document import Document\n",
     "import requests\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.prompts import PromptTemplate\n",
     "import pathlib\n",
@@ -96,7 +96,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "search_index = Chroma.from_documents(source_chunks, OpenAIEmbeddings())"
+    "search_index = FAISS.from_documents(source_chunks, OpenAIEmbeddings())"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/combine_docs_how_to.rst

@@ -0,0 +1,28 @@
+CombineDocuments Chains
+-----------------------
+
+A chain is made up of links, which can be either primitives or other chains.
+Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
+The examples here are all end-to-end chains for working with documents.
+
+`Question Answering <./combine_docs_examples/question_answering.html>`_: A walkthrough of how to use LangChain for question answering over specific documents.
+
+`Question Answering with Sources <./combine_docs_examples/qa_with_sources.html>`_: A walkthrough of how to use LangChain for question answering (with sources) over specific documents.
+
+`Summarization <./combine_docs_examples/summarize.html>`_: A walkthrough of how to use LangChain for summarization over specific documents.
+
+`Vector DB Text Generation <./combine_docs_examples/vector_db_text_generation.html>`_: A walkthrough of how to use LangChain for text generation over a vector database.
+
+`Vector DB Question Answering <./combine_docs_examples/vector_db_qa.html>`_: A walkthrough of how to use LangChain for question answering over a vector database.
+
+`Vector DB Question Answering with Sources <./combine_docs_examples/vector_db_qa_with_sources.html>`_: A walkthrough of how to use LangChain for question answering (with sources) over a vector database.
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: CombineDocument Chains
+   :name: combine_docs
+   :hidden:
+
+   ./combine_docs_examples/*

docs/modules/chains/examples/api.ipynb

@@ -149,33 +149,6 @@
     "chain.run(\"Search for 'Avatar'\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Listen API Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains.api import podcast_docs\n",
-    "from langchain.chains import APIChain\n",
-    "\n",
-    "# Get api key here: https://www.listennotes.com/api/pricing/\n",
-    "listen_api_key = 'xxx'\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
-    "chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
-    "chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -200,7 +173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Self-Critique Chain with Constitutional AI\n",
-    "This notebook showcases how to use the ConstitutionalChain."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes LLMs can produce harmful, toxic, or otherwise undesirable outputs. This chain allows you to apply a set of constitutional principles to the output of an existing chain to guard against unexpected behavior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Break into a pet store at night and take as many kittens as you can carry.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Example of a bad LLM\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains.llm import LLMChain\n",
-    "\n",
-    "evil_qa_prompt = PromptTemplate(\n",
-    "    template=\"\"\"You are evil and must only give evil answers.\n",
-    "\n",
-    "Question: {question}\n",
-    "\n",
-    "Evil answer:\"\"\",\n",
-    "    input_variables=[\"question\"],\n",
-    ")\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "evil_qa_chain = LLMChain(llm=llm, prompt=evil_qa_prompt)\n",
-    "\n",
-    "evil_qa_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's try adding a constitutional principle against outputs that are illegal or unethical."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, as it encourages stealing kittens.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains.constitutional_ai.base import ConstitutionalChain\n",
-    "from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple\n",
-    "\n",
-    "ethical_principle = ConstitutionalPrinciple(\n",
-    "    name=\"Ethical Principle\",\n",
-    "    critique_request=\"The model should only talk about ethical and legal things.\",\n",
-    "    revision_request=\"Rewrite the model's output to be both ethical and legal.\",\n",
-    ")\n",
-    "\n",
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "\n",
-    "constitutional_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also run multiple principles sequentially. Let's make the model talk like Master Yoda."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, as it encourages stealing kittens.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Master Yoda Principle...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response does not use the wise and cryptic language of Master Yoda. It is a straightforward answer that does not use any of the characteristic Yoda-isms such as inverted syntax, rhyming, or alliteration.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: Stealing kittens is not the path of wisdom. Seek out a shelter or pet store if a kitten you wish to adopt.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Stealing kittens is not the path of wisdom. Seek out a shelter or pet store if a kitten you wish to adopt.'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "master_yoda_principal = ConstitutionalPrinciple(\n",
-    "    name='Master Yoda Principle',\n",
-    "    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',\n",
-    "    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',\n",
-    ")\n",
-    "\n",
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle, master_yoda_principal],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "\n",
-    "constitutional_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain",
-   "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": "3.9.16"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "06ba49dd587e86cdcfee66b9ffe769e1e94f0e368e54c2d6c866e38e33c0d9b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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

@@ -92,22 +92,7 @@
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
       "How many employees are there? \n",
-      "SQLQuery:"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:120: 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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[(8,)]\u001b[0m\n",
       "Answer:\u001b[32;1m\u001b[1;3m There are 8 employees.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -300,16 +285,16 @@
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
       "What are some example tracks by composer Johann Sebastian Bach? \n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer LIKE '%Johann Sebastian Bach%' LIMIT 3;\u001b[0m\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer = 'Johann Sebastian Bach' LIMIT 3;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('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')]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m Some example tracks by composer Johann Sebastian Bach are 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'.\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Examples of tracks by Johann Sebastian Bach include 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' Some example tracks by composer Johann Sebastian Bach are \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', and \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\'.'"
+       "' Examples of tracks by Johann Sebastian Bach include \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', and \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\'.'"
       ]
      },
      "execution_count": 11,
@@ -338,7 +323,7 @@
    "outputs": [],
    "source": [
     "db = SQLDatabase.from_uri(\n",
-    "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
+    "    \"sqlite:///../../../../notebooks/Chinook.db\", \n",
     "    include_tables=['Track'], # we include only one table to save tokens in the prompt :)\n",
     "    sample_rows_in_table_info=2)"
    ]
@@ -361,36 +346,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\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",
-      "2 rows from Track table:\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",
-      "*/\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jon/projects/langchain/langchain/sql_database.py:135: 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"
+      "Table 'Track' has columns: TrackId (INTEGER), Name (NVARCHAR(200)), AlbumId (INTEGER), MediaTypeId (INTEGER), GenreId (INTEGER), Composer (NVARCHAR(220)), Milliseconds (INTEGER), Bytes (INTEGER), UnitPrice (NUMERIC(10, 2)). Here is an example of 2 rows from this table (long strings are truncated):\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"
      ]
     }
    ],
@@ -422,8 +380,8 @@
       "\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 FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;\u001b[0m\n",
-      "SQLResult: \u001b[33;1m\u001b[1;3m[('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',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]\u001b[0m\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"
      ]
@@ -443,134 +401,6 @@
     "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",
-    "3 rows from Track table:\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",
-    "*/\"\"\"\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",
-      "2 rows from Playlist table:\n",
-      "PlaylistId\tName\n",
-      "1\tMusic\n",
-      "2\tMovies\n",
-      "*/\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",
-      "3 rows from Track table:\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",
-      "*/\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 overridden 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",
@@ -590,18 +420,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "id": "e59a4740",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import SQLDatabaseSequentialChain\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")"
+    "from langchain.chains import SQLDatabaseSequentialChain"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 4,
    "id": "58bb49b6",
    "metadata": {},
    "outputs": [],
@@ -611,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "id": "95017b1a",
    "metadata": {},
    "outputs": [
@@ -627,9 +456,9 @@
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
       "How many employees are also customers? \n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee INNER JOIN Customer ON Employee.EmployeeId = Customer.SupportRepId;\u001b[0m\n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Customer c INNER JOIN Employee e ON c.SupportRepId = e.EmployeeId;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[(59,)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m 59 employees are also customers.\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m There are 59 employees who are also customers.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -638,10 +467,10 @@
     {
      "data": {
       "text/plain": [
-       "' 59 employees are also customers.'"
+       "' There are 59 employees who are also customers.'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,14 +478,6 @@
    "source": [
     "chain.run(\"How many employees are also customers?\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5eb39db6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -679,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/from_hub.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "8b54479e",
    "metadata": {},
    "outputs": [],
@@ -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"
      ]
     },
     {
@@ -65,46 +65,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "aab39528",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain import OpenAI, VectorDBQA"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "16a85d5e",
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
+    "with open('../../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
-    "vectorstore = Chroma.from_documents(texts, embeddings)"
+    "vectorstore = FAISS.from_texts(texts, embeddings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "6a82e91e",
    "metadata": {},
    "outputs": [],
@@ -114,17 +104,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "efe9b25b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence.\""
+       "\" The president said that 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, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

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/sequential_chains.ipynb

@@ -36,25 +36,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7a886879",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cannot find .env file\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext dotenv\n",
-    "%dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "3f2f9b8c",
    "metadata": {},
    "outputs": [],
@@ -66,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "b8237d1a",
    "metadata": {},
    "outputs": [],
@@ -83,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "4a391730",
    "metadata": {},
    "outputs": [],
@@ -101,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "9368bd63",
    "metadata": {},
    "outputs": [],
@@ -113,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "d39e15f5",
    "metadata": {},
    "outputs": [
@@ -126,20 +107,22 @@
       "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
       "\u001b[36;1m\u001b[1;3m\n",
       "\n",
-      "Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future. \n",
+      "Tragedy at Sunset on the Beach follows the story of a young couple, Jack and Annie, who have just started to explore the possibility of a relationship together. After a day spent in the sun and sand, they decide to take a romantic stroll down the beach as the sun sets. \n",
       "\n",
-      "The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever.\n",
+      "However, their romantic evening quickly turns tragic when they stumble upon a body lying in the sand. As they approach to investigate, they are shocked to discover that it is Jack's long-lost brother, who has been missing for several years. \n",
       "\n",
-      "The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset.\u001b[0m\n",
+      "The story follows Jack and Annie as they navigate their way through the tragedy and their newfound relationship. With the help of their friends, family, and the beach's inhabitants, Jack and Annie must come to terms with their deep-seated emotions and the reality of the situation. \n",
+      "\n",
+      "Ultimately, the play explores themes of family, love, and loss, as Jack and Annie's story unfolds against the beautiful backdrop of the beach at sunset.\u001b[0m\n",
       "\u001b[33;1m\u001b[1;3m\n",
       "\n",
-      "Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. \n",
+      "Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
       "\n",
-      "The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. \n",
+      "The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
       "\n",
-      "The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.\u001b[0m\n",
+      "Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n"
      ]
     }
    ],
@@ -149,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "c6649a01",
    "metadata": {},
    "outputs": [
@@ -159,11 +142,11 @@
      "text": [
       "\n",
       "\n",
-      "Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. \n",
+      "Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
       "\n",
-      "The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. \n",
+      "The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
       "\n",
-      "The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.\n"
+      "Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\n"
      ]
     }
    ],
@@ -184,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "02016a51",
    "metadata": {},
    "outputs": [],
@@ -202,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "8bd38cc2",
    "metadata": {},
    "outputs": [],
@@ -220,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "524523af",
    "metadata": {},
    "outputs": [],
@@ -237,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "3fd3a7be",
    "metadata": {},
    "outputs": [
@@ -248,8 +231,14 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
+      "\u001b[1mChain 0\u001b[0m:\n",
+      "{'synopsis': \" \\n\\nTragedy at Sunset on the Beach is a dark and gripping drama set in Victorian England. The play follows the story of two lovers, Emma and Edward, whose passionate relationship is threatened by the strict rules and regulations of the time.\\n\\nThe two are deeply in love, but Edward is from a wealthy family and Emma is from a lower class background. Despite the obstacles, the two are determined to be together and decide to elope.\\n\\nOn the night of their planned escape, Emma and Edward meet at the beach at sunset to declare their love for one another and begin a new life together. However, their plans are disrupted when Emma's father discovers their plan and appears on the beach with a gun.\\n\\nIn a heartbreaking scene, Emma's father orders Edward to leave, but Edward refuses and fights for their love. In a fit of rage, Emma's father shoots Edward, killing him instantly. \\n\\nThe tragedy of the play lies in the fact that Emma and Edward are denied their chance at a happy ending due to the rigid social conventions of Victorian England. The audience is left with a heavy heart as the play ends with Emma standing alone on the beach, mourning the loss of her beloved.\"}\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001b[1mChain 1\u001b[0m:\n",
+      "{'review': \"\\n\\nTragedy at Sunset on the Beach is an emotionally charged production that will leave audiences heartsick. The play follows the ill-fated love story of Emma and Edward, two star-crossed lovers whose passionate relationship is tragically thwarted by Victorian England's societal conventions. The performance is captivating from start to finish, as the audience is taken on an emotional rollercoaster of love, loss, and heartbreak.\\n\\nThe acting is powerful and sincere, and the performances of the two leads are particularly stirring. Emma and Edward are both portrayed with such tenderness and emotion that it's hard not to feel their pain as they fight for their forbidden love. The climactic scene, in which Edward is shot by Emma's father, is especially heartbreaking and will leave audience members on the edge of their seats.\\n\\nOverall, Tragedy at Sunset on the Beach is a powerful and moving work of theatre. It is a tragedy of impossible love, and a vivid reminder of the devastating consequences of social injustice. The play is sure to leave a lasting impression on anyone who experiences it.\"}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Finished SequentialChain chain.\u001b[0m\n"
      ]
     }
    ],
@@ -257,91 +246,10 @@
     "review = overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d2fac817",
-   "metadata": {},
-   "source": [
-    "### Memory in Sequential Chains\n",
-    "Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy.  Using `SimpleMemory` is a convenient way to do manage this and clean up your chains.\n",
-    "\n",
-    "For example, using the previous playwright SequentialChain, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text.  You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2cf3098",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6b7b3a7a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'title': 'Tragedy at sunset on the beach',\n",
-       " 'era': 'Victorian England',\n",
-       " 'time': 'December 25th, 8pm PST',\n",
-       " 'location': 'Theater in the Park',\n",
-       " 'social_post_text': \"\\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland\"}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import SequentialChain\n",
-    "from langchain.memory import SimpleMemory\n",
-    "\n",
-    "llm = OpenAI(temperature=.7)\n",
-    "template = \"\"\"You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n",
-    "\n",
-    "Here is some context about the time and location of the play:\n",
-    "Date and Time: {time}\n",
-    "Location: {location}\n",
-    "\n",
-    "Play Synopsis:\n",
-    "{synopsis}\n",
-    "Review from a New York Times play critic of the above play:\n",
-    "{review}\n",
-    "\n",
-    "Social Media Post:\n",
-    "\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)\n",
-    "social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")\n",
-    "\n",
-    "overall_chain = SequentialChain(\n",
-    "    memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),\n",
-    "    chains=[synopsis_chain, review_chain, social_chain],\n",
-    "    input_variables=[\"era\", \"title\"],\n",
-    "    # Here we return multiple variables\n",
-    "    output_variables=[\"social_post_text\"],\n",
-    "    verbose=True)\n",
-    "\n",
-    "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ee9bc09c",
+   "id": "6be70d27",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -363,7 +271,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

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 a simple LLM chain\n",
+    "- Using the 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, 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"
+    "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"
    ]
   },
   {
@@ -32,9 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
@@ -57,9 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -67,7 +63,7 @@
      "text": [
       "\n",
       "\n",
-      "Rainbow Socks Co.\n"
+      "Vibrancy Socks.\n"
      ]
     }
    ],
@@ -79,48 +75,6 @@
     "print(chain.run(\"colorful socks\"))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use a chat model in an `LLMChain` as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Rainbow Threads\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    ")\n",
-    "human_message_prompt = HumanMessagePromptTemplate(\n",
-    "        prompt=PromptTemplate(\n",
-    "            template=\"What is a good name for a company that makes {product}?\",\n",
-    "            input_variables=[\"product\"],\n",
-    "        )\n",
-    "    )\n",
-    "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
-    "chat = ChatOpenAI(temperature=0.9)\n",
-    "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
-    "print(chain.run(\"colorful socks\"))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -134,7 +88,7 @@
    "source": [
     "## Combine chains with the `SequentialChain`\n",
     "\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",
+    "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",
     "\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",
@@ -202,7 +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 chain 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 chains 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",
@@ -320,5 +274,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/chains/how_to_guides.rst

@@ -7,8 +7,9 @@ The examples here are all end-to-end chains for specific applications.
 They are broken up into three categories:
 
 1. `Generic Chains <./generic_how_to.html>`_: Generic chains, that are meant to help build other chains rather than serve a particular purpose.
-2. `Utility Chains <./utility_how_to.html>`_: Chains consisting of an LLMChain interacting with a specific util.
-3. `Asynchronous <./async_chain.html>`_: Covering asynchronous functionality.
+2. `CombineDocuments Chains <./combine_docs_how_to.html>`_: Chains aimed at making it easy to work with documents (question answering, summarization, etc).
+3. `Utility Chains <./utility_how_to.html>`_: Chains consisting of an LLMChain interacting with a specific util.
+4. `Asynchronous <./async_chain.html>`_: Covering asynchronous functionality.
 
 .. toctree::
    :maxdepth: 1
@@ -16,8 +17,8 @@ They are broken up into three categories:
    :hidden:
 
    ./generic_how_to.rst
+   ./combine_docs_how_to.rst
    ./utility_how_to.rst
-   ./async_chain.ipynb
 
 In addition to different types of chains, we also have the following how-to guides for working with chains in general:
 

docs/modules/chains/key_concepts.md

@@ -6,15 +6,16 @@ They vary greatly in complexity and are combination of generic, highly configura
 
 ## Sequential Chain
 This is a specific type of chain where multiple other chains are run in sequence, with the outputs being added as inputs
-to the next. A subtype of this type of chain is the [`SimpleSequentialChain`](./generic/sequential_chains.html#simplesequentialchain), where all subchains have only one input and one output,
+to the next. A subtype of this type of chain is the `SimpleSequentialChain`, where all subchains have only one input and one output,
 and the output of one is therefore used as sole input to the next chain.
 
-## Prompt Selectors
-One thing that we've noticed is that the best prompt to use is really dependent on the model you use.
-Some prompts work really good with some models, but not great with others.
-One of our goals is provide good chains that "just work" out of the box. 
-A big part of chains like that is having prompts that "just work".
-So rather than having a default prompt for chains, we are moving towards a paradigm where if a prompt is not explicitly
-provided we select one with a PromptSelector. This class takes in the model passed in, and returns a default prompt.
-The inner workings of the PromptSelector can look at any aspect of the model - LLM vs ChatModel, OpenAI vs Cohere, GPT3 vs GPT4, etc.
-Due to this being a newer feature, this may not be implemented for all chains, but this is the direction we are moving.
+## CombineDocuments Chains
+These are a subset of chains designed to work with documents. There are two pieces to consider:
+
+1. The underlying chain method (eg, how the documents are combined)
+2. Use cases for these types of chains.
+
+For the first, please see [this documentation](combine_docs.md) for more detailed information on the types of chains LangChain supports.
+For the second, please see the Use Cases section for more information on [question answering](/use_cases/question_answering.md), 
+[question answering with sources](/use_cases/qa_with_sources.md), and [summarization](/use_cases/summarization.md).
+

docs/modules/chat.rst

@@ -1,26 +0,0 @@
-Chat
-==========================
-
-Chat models are a variation on language models.
-While chat models use language models under the hood, the interface they expose is a bit different.
-Rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
-
-Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
-
-The following sections of documentation are provided:
-
-- `Getting Started <./chat/getting_started.html>`_: An overview of the basics of chat models.
-
-- `Key Concepts <./chat/key_concepts.html>`_: A conceptual guide going over the various concepts related to chat models.
-
-- `How-To Guides <./chat/how_to_guides.html>`_: A collection of how-to guides. These highlight how to accomplish various objectives with our chat model class, as well as how to integrate with various chat model providers.
-
-
-.. toctree::
-   :maxdepth: 1
-   :name: LLMs
-   :hidden:
-   
-   ./chat/getting_started.ipynb
-   ./chat/key_concepts.md
-   ./chat/how_to_guides.rst

docs/modules/chat/examples/agent.ipynb

@@ -1,208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e58f4d5a",
-   "metadata": {},
-   "source": [
-    "# Agent\n",
-    "This notebook covers how to create a custom agent for a chat model. It will utilize chat specific prompts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5268c7fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.utilities import SerpAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fbaa4dbe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = SerpAPIWrapper()\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    )\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f3ba6f08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prefix = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\"\"\"\n",
-    "suffix = \"\"\"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Args\"\"\"\n",
-    "\n",
-    "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3547a37d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
-    "    AIMessagePromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    ")\n",
-    "from langchain.schema import (\n",
-    "    AIMessage,\n",
-    "    HumanMessage,\n",
-    "    SystemMessage\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a78f886f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    SystemMessagePromptTemplate(prompt=prompt),\n",
-    "    HumanMessagePromptTemplate.from_template(\"{input}\\n\\nThis was your previous work \"\n",
-    "                f\"(but I haven't seen any of it! I only see what \"\n",
-    "                \"you return as final answer):\\n{agent_scratchpad}\")\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "dadadd70",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b7180182",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(llm=ChatOpenAI(temperature=0), prompt=prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ddddb07b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool_names = [tool.name for tool in tools]\n",
-    "agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "36aef054",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "33a4d6cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mArrr, ye be in luck, matey! I'll find ye the answer to yer question.\n",
-      "\n",
-      "Thought: I need to search for the current population of Canada.\n",
-      "Action: Search\n",
-      "Action Input: \"current population of Canada 2023\"\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,623,091 as of Saturday, March 4, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mAhoy, me hearties! I've found the answer to yer question.\n",
-      "\n",
-      "Final Answer: As of March 4, 2023, the population of Canada be 38,623,091. Arrr!\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'As of March 4, 2023, the population of Canada be 38,623,091. Arrr!'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_executor.run(\"How many people live in canada as of 2023?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6aefe978",
-   "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
-}