\",\n",
diff --git a/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb b/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
index a7d8fb46746..3a18c047a29 100644
--- a/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
+++ b/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
@@ -34,7 +34,7 @@
"os.environ['O365_CLIENT_SECRET'] = \"YOUR CLIENT SECRET\"\n",
"```\n",
"\n",
- "This loader uses an authentication called [*on behalf of a user*](https://learn.microsoft.com/en-us/graph/auth-v2-user?context=graph%2Fapi%2F1.0&view=graph-rest-1.0). It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was succesful.\n",
+ "This loader uses an authentication called [*on behalf of a user*](https://learn.microsoft.com/en-us/graph/auth-v2-user?context=graph%2Fapi%2F1.0&view=graph-rest-1.0). It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was successful.\n",
"\n",
"\n",
"```python\n",
diff --git a/docs/docs/integrations/document_loaders/source_code.ipynb b/docs/docs/integrations/document_loaders/source_code.ipynb
index 78e375617de..19281a59b5c 100644
--- a/docs/docs/integrations/document_loaders/source_code.ipynb
+++ b/docs/docs/integrations/document_loaders/source_code.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Source Code\n",
"\n",
- "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a seperate document.\n",
+ "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a separate document.\n",
"\n",
"This approach can potentially improve the accuracy of QA models over source code. Currently, the supported languages for code parsing are Python and JavaScript. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax."
]
diff --git a/docs/docs/integrations/document_loaders/weather.ipynb b/docs/docs/integrations/document_loaders/weather.ipynb
index 44f90612a06..9ed6f0d2b87 100644
--- a/docs/docs/integrations/document_loaders/weather.ipynb
+++ b/docs/docs/integrations/document_loaders/weather.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Weather\n",
"\n",
- ">[OpenWeatherMap](https://openweathermap.org/) is an open source weather service provider\n",
+ ">[OpenWeatherMap](https://openweathermap.org/) is an open-source weather service provider\n",
"\n",
"This loader fetches the weather data from the OpenWeatherMap's OneCall API, using the pyowm Python package. You must initialize the loader with your OpenWeatherMap API token and the names of the cities you want the weather data for."
]
diff --git a/docs/docs/integrations/llms/azure_ml.ipynb b/docs/docs/integrations/llms/azure_ml.ipynb
index 38c1cd2aea5..4443a53d797 100644
--- a/docs/docs/integrations/llms/azure_ml.ipynb
+++ b/docs/docs/integrations/llms/azure_ml.ipynb
@@ -46,7 +46,7 @@
"* `HFContentFormatter`: Formats request and response data for text-generation Hugging Face models\n",
"* `LLamaContentFormatter`: Formats request and response data for LLaMa2\n",
"\n",
- "*Note: `OSSContentFormatter` is being deprecated and replaced with `GPT2ContentFormatter`. The logic is the same but `GPT2ContentFormatter` is a more suitable name. You can still continue to use `OSSContentFormatter` as the changes are backwards compatibile.*\n",
+ "*Note: `OSSContentFormatter` is being deprecated and replaced with `GPT2ContentFormatter`. The logic is the same but `GPT2ContentFormatter` is a more suitable name. You can still continue to use `OSSContentFormatter` as the changes are backwards compatible.*\n",
"\n",
"Below is an example using a summarization model from Hugging Face."
]
diff --git a/docs/docs/integrations/llms/baidu_qianfan_endpoint.ipynb b/docs/docs/integrations/llms/baidu_qianfan_endpoint.ipynb
index 46a8deeebb7..cf5e7eed018 100644
--- a/docs/docs/integrations/llms/baidu_qianfan_endpoint.ipynb
+++ b/docs/docs/integrations/llms/baidu_qianfan_endpoint.ipynb
@@ -7,13 +7,13 @@
"source": [
"# Baidu Qianfan\n",
"\n",
- "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
+ "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
"\n",
"Basically, those model are split into the following type:\n",
"\n",
"- Embedding\n",
"- Chat\n",
- "- Coompletion\n",
+ "- Completion\n",
"\n",
"In this notebook, we will introduce how to use langchain with [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) mainly in `Completion` corresponding\n",
" to the package `langchain/llms` in langchain:\n",
@@ -24,7 +24,7 @@
"\n",
"To use the LLM services based on Baidu Qianfan, you have to initialize these parameters:\n",
"\n",
- "You could either choose to init the AK,SK in enviroment variables or init params:\n",
+ "You could either choose to init the AK,SK in environment variables or init params:\n",
"\n",
"```base\n",
"export QIANFAN_AK=XXX\n",
@@ -158,7 +158,7 @@
"In the case you want to deploy your own model based on EB or serval open sources model, you could follow these steps:\n",
"\n",
"- 1. ďźOptional, if the model are included in the default models, skip itďźDeploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
- "- 2. Set up the field called `endpoint` in the initlization:"
+ "- 2. Set up the field called `endpoint` in the initialization:"
]
},
{
diff --git a/docs/docs/integrations/llms/ctranslate2.ipynb b/docs/docs/integrations/llms/ctranslate2.ipynb
index dd0bf0978e0..3612c59980d 100644
--- a/docs/docs/integrations/llms/ctranslate2.ipynb
+++ b/docs/docs/integrations/llms/ctranslate2.ipynb
@@ -50,7 +50,7 @@
}
],
"source": [
- "# converstion can take several minutes\n",
+ "# conversation can take several minutes\n",
"!ct2-transformers-converter --model meta-llama/Llama-2-7b-hf --quantization bfloat16 --output_dir ./llama-2-7b-ct2 --force"
]
},
diff --git a/docs/docs/integrations/llms/deepinfra.ipynb b/docs/docs/integrations/llms/deepinfra.ipynb
index ecb0fd63918..5e9f0dea418 100644
--- a/docs/docs/integrations/llms/deepinfra.ipynb
+++ b/docs/docs/integrations/llms/deepinfra.ipynb
@@ -28,7 +28,8 @@
"source": [
"import os\n",
"from langchain.llms import DeepInfra\n",
- "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain"
+ "from langchain.prompts import PromptTemplate\n",
+ "from langchain.chains import LLMChain"
]
},
{
@@ -50,7 +51,7 @@
},
"outputs": [
{
- "name": "stdin",
+ "name": "stdout",
"output_type": "stream",
"text": [
" ¡¡¡¡¡¡¡¡\n"
@@ -81,7 +82,7 @@
"metadata": {},
"source": [
"## Create the DeepInfra instance\n",
- "You can also use our open source [deepctl tool](https://github.com/deepinfra/deepctl#deepctl) to manage your model deployments. You can view a list of available parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API)."
+ "You can also use our open-source [deepctl tool](https://github.com/deepinfra/deepctl#deepctl) to manage your model deployments. You can view a list of available parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API)."
]
},
{
diff --git a/docs/docs/integrations/llms/forefrontai.ipynb b/docs/docs/integrations/llms/forefrontai.ipynb
index 5f988c25c9a..7dac40fd7c3 100644
--- a/docs/docs/integrations/llms/forefrontai.ipynb
+++ b/docs/docs/integrations/llms/forefrontai.ipynb
@@ -7,7 +7,7 @@
"# ForefrontAI\n",
"\n",
"\n",
- "The `Forefront` platform gives you the ability to fine-tune and use [open source large language models](https://docs.forefront.ai/forefront/master/models).\n",
+ "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/forefront/master/models).\n",
"\n",
"This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/).\n"
]
@@ -27,7 +27,8 @@
"source": [
"import os\n",
"from langchain.llms import ForefrontAI\n",
- "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain"
+ "from langchain.prompts import PromptTemplate\n",
+ "from langchain.chains import LLMChain"
]
},
{
diff --git a/docs/docs/integrations/llms/gradient.ipynb b/docs/docs/integrations/llms/gradient.ipynb
index 6951db89487..6152e7b03f8 100644
--- a/docs/docs/integrations/llms/gradient.ipynb
+++ b/docs/docs/integrations/llms/gradient.ipynb
@@ -61,7 +61,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Optional: Validate your Enviroment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models."
+ "Optional: Validate your Environment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models."
]
},
{
diff --git a/docs/docs/integrations/llms/manifest.ipynb b/docs/docs/integrations/llms/manifest.ipynb
index 1238ae84f45..514c3f84726 100644
--- a/docs/docs/integrations/llms/manifest.ipynb
+++ b/docs/docs/integrations/llms/manifest.ipynb
@@ -15,7 +15,7 @@
"id": "59fcaebc",
"metadata": {},
"source": [
- "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest\n",
+ "For more detailed information on `manifest`, and how to use it with local huggingface models like in this example, see https://github.com/HazyResearch/manifest\n",
"\n",
"Another example of [using Manifest with Langchain](https://github.com/HazyResearch/manifest/blob/main/examples/langchain_chatgpt.html)."
]
diff --git a/docs/docs/integrations/llms/mosaicml.ipynb b/docs/docs/integrations/llms/mosaicml.ipynb
index 08d378cdccf..141bfe71217 100644
--- a/docs/docs/integrations/llms/mosaicml.ipynb
+++ b/docs/docs/integrations/llms/mosaicml.ipynb
@@ -7,7 +7,7 @@
"source": [
"# MosaicML\n",
"\n",
- "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
+ "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open-source models, or deploy your own.\n",
"\n",
"This example goes over how to use LangChain to interact with MosaicML Inference for text completion."
]
diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb
index 358f0dde79d..e395603138d 100644
--- a/docs/docs/integrations/llms/ollama.ipynb
+++ b/docs/docs/integrations/llms/ollama.ipynb
@@ -334,7 +334,7 @@
"source": [
"## Using the Hub for prompt management\n",
" \n",
- "Open source models often benefit from specific prompts. \n",
+ "Open-source models often benefit from specific prompts. \n",
"\n",
"For example, [Mistral 7b](https://mistral.ai/news/announcing-mistral-7b/) was fine-tuned for chat using the prompt format shown [here](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).\n",
"\n",
diff --git a/docs/docs/integrations/llms/predibase.ipynb b/docs/docs/integrations/llms/predibase.ipynb
index bd208a4345f..ec1924b12a1 100644
--- a/docs/docs/integrations/llms/predibase.ipynb
+++ b/docs/docs/integrations/llms/predibase.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Predibase\n",
"\n",
- "[Predibase](https://predibase.com/) allows you to train, finetune, and deploy any ML modelâfrom linear regression to large language model. \n",
+ "[Predibase](https://predibase.com/) allows you to train, fine-tune, and deploy any ML modelâfrom linear regression to large language model. \n",
"\n",
"This example demonstrates using Langchain with models deployed on Predibase"
]
diff --git a/docs/docs/integrations/llms/promptlayer_openai.ipynb b/docs/docs/integrations/llms/promptlayer_openai.ipynb
index 685deca3d8a..6c2404bb5fc 100644
--- a/docs/docs/integrations/llms/promptlayer_openai.ipynb
+++ b/docs/docs/integrations/llms/promptlayer_openai.ipynb
@@ -180,7 +180,7 @@
"metadata": {},
"source": [
"## Using PromptLayer Track\n",
- "If you would like to use any of the [PromptLayer tracking features](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9), you need to pass the argument `return_pl_id` when instantializing the PromptLayer LLM to get the request id. "
+ "If you would like to use any of the [PromptLayer tracking features](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9), you need to pass the argument `return_pl_id` when instantiating the PromptLayer LLM to get the request id. "
]
},
{
diff --git a/docs/docs/integrations/llms/titan_takeoff.ipynb b/docs/docs/integrations/llms/titan_takeoff.ipynb
index 9d79166000f..fe4cdc8a640 100644
--- a/docs/docs/integrations/llms/titan_takeoff.ipynb
+++ b/docs/docs/integrations/llms/titan_takeoff.ipynb
@@ -44,10 +44,10 @@
"## Choose a Model\n",
"Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
"\n",
- "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
+ "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open-source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
"\n",
"## Taking off\n",
- "Models are referred to by their model id on HuggingFace. Takeoff uses port 8000 by default, but can be configured to use another port. There is also support to use a Nvidia GPU by specifing cuda for the device flag.\n",
+ "Models are referred to by their model id on HuggingFace. Takeoff uses port 8000 by default, but can be configured to use another port. There is also support to use a Nvidia GPU by specifying cuda for the device flag.\n",
"\n",
"To start the takeoff server, run:"
]
diff --git a/docs/docs/integrations/providers/chaindesk.mdx b/docs/docs/integrations/providers/chaindesk.mdx
index 202d9ad602f..7cfd5e96b88 100644
--- a/docs/docs/integrations/providers/chaindesk.mdx
+++ b/docs/docs/integrations/providers/chaindesk.mdx
@@ -1,6 +1,6 @@
# Chaindesk
->[Chaindesk](https://chaindesk.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
+>[Chaindesk](https://chaindesk.ai) is an [open-source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
## Installation and Setup
diff --git a/docs/docs/integrations/providers/clearml_tracking.ipynb b/docs/docs/integrations/providers/clearml_tracking.ipynb
index 00add9bf867..e6bd0e953db 100644
--- a/docs/docs/integrations/providers/clearml_tracking.ipynb
+++ b/docs/docs/integrations/providers/clearml_tracking.ipynb
@@ -570,7 +570,7 @@
"\n",
"- If you close the ClearML Callback using `clearml_callback.flush_tracker(..., finish=True)` the Callback cannot be used anymore. Make a new one if you want to keep logging.\n",
"\n",
- "- Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
+ "- Check out the rest of the open-source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
]
},
{
diff --git a/docs/docs/integrations/providers/cnosdb.mdx b/docs/docs/integrations/providers/cnosdb.mdx
index 60cadd28204..d93f2e0f4c3 100644
--- a/docs/docs/integrations/providers/cnosdb.mdx
+++ b/docs/docs/integrations/providers/cnosdb.mdx
@@ -1,5 +1,5 @@
# CnosDB
-> [CnosDB](https://github.com/cnosdb/cnosdb) is an open source distributed time series database with high performance, high compression rate and high ease of use.
+> [CnosDB](https://github.com/cnosdb/cnosdb) is an open-source distributed time series database with high performance, high compression rate and high ease of use.
## Installation and Setup
diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
index 41d86b1f5ee..6dbabbce60c 100644
--- a/docs/docs/integrations/providers/databricks.md
+++ b/docs/docs/integrations/providers/databricks.md
@@ -19,7 +19,7 @@ See the notebook [Connect to Databricks](/docs/use_cases/qa_structured/integrati
Databricks MLflow integrates with LangChain
-------------------------------------------
-MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](/docs/integrations/providers/mlflow_tracking) for details about MLflow's integration with LangChain.
+MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](/docs/integrations/providers/mlflow_tracking) for details about MLflow's integration with LangChain.
Databricks provides a fully managed and hosted version of MLflow integrated with enterprise security features, high availability, and other Databricks workspace features such as experiment and run management and notebook revision capture. MLflow on Databricks offers an integrated experience for tracking and securing machine learning model training runs and running machine learning projects. See [MLflow guide](https://docs.databricks.com/mlflow/index.html) for more details.
diff --git a/docs/docs/integrations/providers/doctran.mdx b/docs/docs/integrations/providers/doctran.mdx
index 44e899bf5ee..98848b8a0a0 100644
--- a/docs/docs/integrations/providers/doctran.mdx
+++ b/docs/docs/integrations/providers/doctran.mdx
@@ -1,6 +1,6 @@
# Doctran
->[Doctran](https://github.com/psychic-api/doctran) is a python package. It uses LLMs and open source
+>[Doctran](https://github.com/psychic-api/doctran) is a python package. It uses LLMs and open-source
> NLP libraries to transform raw text into clean, structured, information-dense documents
> that are optimized for vector space retrieval. You can think of `Doctran` as a black box where
> messy strings go in and nice, clean, labelled strings come out.
diff --git a/docs/docs/integrations/providers/helicone.mdx b/docs/docs/integrations/providers/helicone.mdx
index df9b3bde706..0e76b9604be 100644
--- a/docs/docs/integrations/providers/helicone.mdx
+++ b/docs/docs/integrations/providers/helicone.mdx
@@ -4,7 +4,7 @@ This page covers how to use the [Helicone](https://helicone.ai) ecosystem within
## 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 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.
diff --git a/docs/docs/integrations/providers/hologres.mdx b/docs/docs/integrations/providers/hologres.mdx
index 02b13540dae..44ecf3008b1 100644
--- a/docs/docs/integrations/providers/hologres.mdx
+++ b/docs/docs/integrations/providers/hologres.mdx
@@ -4,7 +4,7 @@
>`Hologres` supports standard `SQL` syntax, is compatible with `PostgreSQL`, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services.
>`Hologres` provides **vector database** functionality by adopting [Proxima](https://www.alibabacloud.com/help/en/hologres/latest/vector-processing).
->`Proxima` is a high-performance software library developed by `Alibaba DAMO Academy`. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.
+>`Proxima` is a high-performance software library developed by `Alibaba DAMO Academy`. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open-source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.
## Installation and Setup
diff --git a/docs/docs/integrations/providers/log10.mdx b/docs/docs/integrations/providers/log10.mdx
index a4d634566d2..d458435e552 100644
--- a/docs/docs/integrations/providers/log10.mdx
+++ b/docs/docs/integrations/providers/log10.mdx
@@ -4,7 +4,7 @@ This page covers how to use the [Log10](https://log10.io) within LangChain.
## What is Log10?
-Log10 is an [open source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.
+Log10 is an [open-source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.
## Quick start
diff --git a/docs/docs/integrations/providers/promptlayer.mdx b/docs/docs/integrations/providers/promptlayer.mdx
index 923b2a3dc47..1a7b1cb9262 100644
--- a/docs/docs/integrations/providers/promptlayer.mdx
+++ b/docs/docs/integrations/providers/promptlayer.mdx
@@ -43,7 +43,7 @@ You can use the PromptLayer request ID to add a prompt, score, or other metadata
This LLM is identical to the [OpenAI](/docs/ecosystem/integrations/openai.html) LLM, except that
- all your requests will be logged to your PromptLayer account
- you can add `pl_tags` when instantiating 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).
+- you can add `return_pl_id` when instantiating to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](/docs/integrations/chat/promptlayer_chatopenai.html) and `PromptLayerOpenAIChat`
diff --git a/docs/docs/integrations/providers/redis.mdx b/docs/docs/integrations/providers/redis.mdx
index 92c9f963e82..445bf3b19ca 100644
--- a/docs/docs/integrations/providers/redis.mdx
+++ b/docs/docs/integrations/providers/redis.mdx
@@ -54,7 +54,7 @@ The only way to use a Redis Cluster is with LangChain classes accepting a precon
The Cache wrapper allows for [Redis](https://redis.io) to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.
#### Standard Cache
-The standard cache is the Redis bread & butter of use case in production for both [open source](https://redis.io) and [enterprise](https://redis.com) users globally.
+The standard cache is the Redis bread & butter of use case in production for both [open-source](https://redis.io) and [enterprise](https://redis.com) users globally.
To import this cache:
```python
diff --git a/docs/docs/integrations/providers/sklearn.mdx b/docs/docs/integrations/providers/sklearn.mdx
index 09bd746a5b7..341bb671d0a 100644
--- a/docs/docs/integrations/providers/sklearn.mdx
+++ b/docs/docs/integrations/providers/sklearn.mdx
@@ -1,6 +1,6 @@
# scikit-learn
->[scikit-learn](https://scikit-learn.org/stable/) is an open source collection of machine learning algorithms,
+>[scikit-learn](https://scikit-learn.org/stable/) is an open-source collection of machine learning algorithms,
> including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
## Installation and Setup
diff --git a/docs/docs/integrations/providers/supabase.mdx b/docs/docs/integrations/providers/supabase.mdx
index a57b7079b6c..5c7d89eabbb 100644
--- a/docs/docs/integrations/providers/supabase.mdx
+++ b/docs/docs/integrations/providers/supabase.mdx
@@ -1,6 +1,6 @@
# Supabase (Postgres)
->[Supabase](https://supabase.com/docs) is an open source `Firebase` alternative.
+>[Supabase](https://supabase.com/docs) is an open-source `Firebase` alternative.
> `Supabase` is built on top of `PostgreSQL`, which offers strong `SQL`
> querying capabilities and enables a simple interface with already-existing tools and frameworks.
diff --git a/docs/docs/integrations/providers/tigris.mdx b/docs/docs/integrations/providers/tigris.mdx
index 08c79f09516..4485e8379ab 100644
--- a/docs/docs/integrations/providers/tigris.mdx
+++ b/docs/docs/integrations/providers/tigris.mdx
@@ -1,6 +1,6 @@
# Tigris
-> [Tigris](https://tigrisdata.com) is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
+> [Tigris](https://tigrisdata.com) is an open-source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
> `Tigris` eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.
## Installation and Setup
diff --git a/docs/docs/integrations/providers/trulens.mdx b/docs/docs/integrations/providers/trulens.mdx
index 8748d19b44b..97dc0c14a24 100644
--- a/docs/docs/integrations/providers/trulens.mdx
+++ b/docs/docs/integrations/providers/trulens.mdx
@@ -4,7 +4,7 @@ This page covers how to use [TruLens](https://trulens.org) to evaluate and track
## What is TruLens?
-TruLens is an [opensource](https://github.com/truera/trulens) package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
+TruLens is an [open-source](https://github.com/truera/trulens) package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
## Quick start
diff --git a/docs/docs/integrations/providers/typesense.mdx b/docs/docs/integrations/providers/typesense.mdx
index 55ceb08eafa..62d5581139e 100644
--- a/docs/docs/integrations/providers/typesense.mdx
+++ b/docs/docs/integrations/providers/typesense.mdx
@@ -1,6 +1,6 @@
# Typesense
-> [Typesense](https://typesense.org) is an open source, in-memory search engine, that you can either
+> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either
> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run
> on [Typesense Cloud](https://cloud.typesense.org/).
> `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also
diff --git a/docs/docs/integrations/providers/vectara/vectara_chat.ipynb b/docs/docs/integrations/providers/vectara/vectara_chat.ipynb
index a706103062a..206c4d42323 100644
--- a/docs/docs/integrations/providers/vectara/vectara_chat.ipynb
+++ b/docs/docs/integrations/providers/vectara/vectara_chat.ipynb
@@ -155,7 +155,7 @@
"metadata": {},
"outputs": [],
"source": [
- "query = \"Did he mention who she suceeded\"\n",
+ "query = \"Did he mention who she succeeded\"\n",
"result = qa({\"question\": query})"
]
},
@@ -267,7 +267,7 @@
"outputs": [],
"source": [
"chat_history = [(query, result[\"answer\"])]\n",
- "query = \"Did he mention who she suceeded\"\n",
+ "query = \"Did he mention who she succeeded\"\n",
"result = qa({\"question\": query, \"chat_history\": chat_history})"
]
},
@@ -656,7 +656,7 @@
],
"source": [
"chat_history = [(query, result[\"answer\"])]\n",
- "query = \"Did he mention who she suceeded\"\n",
+ "query = \"Did he mention who she succeeded\"\n",
"result = qa({\"question\": query, \"chat_history\": chat_history})"
]
},
diff --git a/docs/docs/integrations/providers/weather.mdx b/docs/docs/integrations/providers/weather.mdx
index 20623489c40..5d557190b98 100644
--- a/docs/docs/integrations/providers/weather.mdx
+++ b/docs/docs/integrations/providers/weather.mdx
@@ -1,6 +1,6 @@
# Weather
->[OpenWeatherMap](https://openweathermap.org/) is an open source weather service provider.
+>[OpenWeatherMap](https://openweathermap.org/) is an open-source weather service provider.
diff --git a/docs/docs/integrations/providers/weaviate.mdx b/docs/docs/integrations/providers/weaviate.mdx
index e68105bf6f0..1c358ec6c64 100644
--- a/docs/docs/integrations/providers/weaviate.mdx
+++ b/docs/docs/integrations/providers/weaviate.mdx
@@ -9,7 +9,7 @@ What is `Weaviate`?
- Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.
- Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.
- Weaviate has a GraphQL-API to access your data easily.
-- We aim to bring your vector search set up to production to query in mere milliseconds (check our [open source benchmarks](https://weaviate.io/developers/weaviate/current/benchmarks/) to see if Weaviate fits your use case).
+- We aim to bring your vector search set up to production to query in mere milliseconds (check our [open-source benchmarks](https://weaviate.io/developers/weaviate/current/benchmarks/) to see if Weaviate fits your use case).
- Get to know Weaviate in the [basics getting started guide](https://weaviate.io/developers/weaviate/current/core-knowledge/basics.html) in under five minutes.
**Weaviate in detail:**
diff --git a/docs/docs/integrations/retrievers/azure_cognitive_search.ipynb b/docs/docs/integrations/retrievers/azure_cognitive_search.ipynb
index 9b09e63464b..5a3c2ca283b 100644
--- a/docs/docs/integrations/retrievers/azure_cognitive_search.ipynb
+++ b/docs/docs/integrations/retrievers/azure_cognitive_search.ipynb
@@ -26,7 +26,7 @@
"source": [
"## Set up Azure Cognitive Search\n",
"\n",
- "To set up ACS, please follow the instrcutions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n",
+ "To set up ACS, please follow the instructions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n",
"\n",
"Please note\n",
"1. the name of your ACS service, \n",
diff --git a/docs/docs/integrations/retrievers/cohere-reranker.ipynb b/docs/docs/integrations/retrievers/cohere-reranker.ipynb
index 6c2c25c9cbc..7f18cd00f09 100644
--- a/docs/docs/integrations/retrievers/cohere-reranker.ipynb
+++ b/docs/docs/integrations/retrievers/cohere-reranker.ipynb
@@ -137,7 +137,7 @@
"\n",
"Iâve worked on these issues a long time. \n",
"\n",
- "I know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety. \n",
+ "I know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety. \n",
"\n",
"So letâs not abandon our streets. Or choose between safety and equal justice.\n",
"----------------------------------------------------------------------------------------------------\n",
@@ -373,7 +373,7 @@
"\n",
"Iâve worked on these issues a long time. \n",
"\n",
- "I know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety. \n",
+ "I know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety. \n",
"\n",
"So letâs not abandon our streets. Or choose between safety and equal justice.\n",
"----------------------------------------------------------------------------------------------------\n",
diff --git a/docs/docs/integrations/retrievers/merger_retriever.ipynb b/docs/docs/integrations/retrievers/merger_retriever.ipynb
index 0189c2d46d2..f0e7bdc6541 100644
--- a/docs/docs/integrations/retrievers/merger_retriever.ipynb
+++ b/docs/docs/integrations/retrievers/merger_retriever.ipynb
@@ -157,7 +157,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# You can use an additional document transformer to reorder documents after removing redudance.\n",
+ "# You can use an additional document transformer to reorder documents after removing redundance.\n",
"from langchain.document_transformers import LongContextReorder\n",
"\n",
"filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)\n",
diff --git a/docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb b/docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb
index 0eacf0554c9..4353f47364d 100644
--- a/docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb
+++ b/docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb
@@ -11,7 +11,7 @@
"\n",
"This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.\n",
"\n",
- "The logic of this retriever is taken from [this documentaion](https://docs.pinecone.io/docs/hybrid-search)\n",
+ "The logic of this retriever is taken from [this documentation](https://docs.pinecone.io/docs/hybrid-search)\n",
"\n",
"To use Pinecone, you must have an API key and an Environment. \n",
"Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart)."
@@ -140,7 +140,7 @@
" dimension=1536, # dimensionality of dense model\n",
" metric=\"dotproduct\", # sparse values supported only for dotproduct\n",
" pod_type=\"s1\",\n",
- " metadata_config={\"indexed\": []}, # see explaination above\n",
+ " metadata_config={\"indexed\": []}, # see explanation above\n",
")"
]
},
diff --git a/docs/docs/integrations/retrievers/weaviate-hybrid.ipynb b/docs/docs/integrations/retrievers/weaviate-hybrid.ipynb
index f256d49d068..bdbf7a92156 100644
--- a/docs/docs/integrations/retrievers/weaviate-hybrid.ipynb
+++ b/docs/docs/integrations/retrievers/weaviate-hybrid.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Weaviate Hybrid Search\n",
"\n",
- ">[Weaviate](https://weaviate.io/developers/weaviate) is an open source vector database.\n",
+ ">[Weaviate](https://weaviate.io/developers/weaviate) is an open-source vector database.\n",
"\n",
">[Hybrid search](https://weaviate.io/blog/hybrid-search-explained) is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results. It uses the best features of both keyword-based search algorithms with vector search techniques.\n",
"\n",
diff --git a/docs/docs/integrations/text_embedding/baidu_qianfan_endpoint.ipynb b/docs/docs/integrations/text_embedding/baidu_qianfan_endpoint.ipynb
index 5272f235309..e971b038914 100644
--- a/docs/docs/integrations/text_embedding/baidu_qianfan_endpoint.ipynb
+++ b/docs/docs/integrations/text_embedding/baidu_qianfan_endpoint.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Baidu Qianfan\n",
"\n",
- "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
+ "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
"\n",
"Basically, those model are split into the following type:\n",
"\n",
@@ -24,7 +24,7 @@
"\n",
"To use the LLM services based on Baidu Qianfan, you have to initialize these parameters:\n",
"\n",
- "You could either choose to init the AK,SK in enviroment variables or init params:\n",
+ "You could either choose to init the AK,SK in environment variables or init params:\n",
"\n",
"```base\n",
"export QIANFAN_AK=XXX\n",
@@ -97,7 +97,7 @@
"In the case you want to deploy your own model based on Ernie Bot or third-party open sources model, you could follow these steps:\n",
"\n",
"- 1. ďźOptional, if the model are included in the default models, skip itďźDeploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
- "- 2. Set up the field called `endpoint` in the initlization:"
+ "- 2. Set up the field called `endpoint` in the initialization:"
]
},
{
diff --git a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
index 4c0c515e806..10883967908 100644
--- a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
@@ -8,7 +8,7 @@
"\n",
">[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. \n",
"\n",
- "Note: This integration is seperate from the Google PaLM integration.\n",
+ "Note: This integration is separate from the Google PaLM integration.\n",
"\n",
"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
"\n",
diff --git a/docs/docs/integrations/text_embedding/gradient.ipynb b/docs/docs/integrations/text_embedding/gradient.ipynb
index f95a0686b90..fa8e192958c 100644
--- a/docs/docs/integrations/text_embedding/gradient.ipynb
+++ b/docs/docs/integrations/text_embedding/gradient.ipynb
@@ -57,7 +57,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Optional: Validate your Environment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models. Using the `gradientai` Python package."
+ "Optional: Validate your environment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models. Using the `gradientai` Python package."
]
},
{
diff --git a/docs/docs/integrations/text_embedding/mosaicml.ipynb b/docs/docs/integrations/text_embedding/mosaicml.ipynb
index 24d7aecb724..8119d9f5f5c 100644
--- a/docs/docs/integrations/text_embedding/mosaicml.ipynb
+++ b/docs/docs/integrations/text_embedding/mosaicml.ipynb
@@ -6,7 +6,7 @@
"source": [
"# MosaicML\n",
"\n",
- ">[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
+ ">[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open-source models, or deploy your own.\n",
"\n",
"This example goes over how to use LangChain to interact with `MosaicML` Inference for text embedding."
]
diff --git a/docs/docs/integrations/toolkits/clickup.ipynb b/docs/docs/integrations/toolkits/clickup.ipynb
index 46e83a46a92..3b856606743 100644
--- a/docs/docs/integrations/toolkits/clickup.ipynb
+++ b/docs/docs/integrations/toolkits/clickup.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# ClickUp Langchiain Toolkit"
+ "# ClickUp Langchain Toolkit"
]
},
{
diff --git a/docs/docs/integrations/toolkits/github.ipynb b/docs/docs/integrations/toolkits/github.ipynb
index 36d13cb7f71..f72c0c8a60a 100644
--- a/docs/docs/integrations/toolkits/github.ipynb
+++ b/docs/docs/integrations/toolkits/github.ipynb
@@ -15,11 +15,11 @@
"3. Set your environmental variables\n",
"4. Pass the tools to your agent with `toolkit.get_tools()`\n",
"\n",
- "Each of these steps will be explained in greate detail below.\n",
+ "Each of these steps will be explained in great detail below.\n",
"\n",
"1. **Get Issues**- fetches issues from the repository.\n",
"\n",
- "2. **Get Issue**- feteches details about a specific issue.\n",
+ "2. **Get Issue**- fetches details about a specific issue.\n",
"\n",
"3. **Comment on Issue**- posts a comment on a specific issue.\n",
"\n",
diff --git a/docs/docs/integrations/toolkits/gitlab.ipynb b/docs/docs/integrations/toolkits/gitlab.ipynb
index a8f28f09fa3..cabf3dae7e3 100644
--- a/docs/docs/integrations/toolkits/gitlab.ipynb
+++ b/docs/docs/integrations/toolkits/gitlab.ipynb
@@ -15,11 +15,11 @@
"3. Set your environmental variables\n",
"4. Pass the tools to your agent with `toolkit.get_tools()`\n",
"\n",
- "Each of these steps will be explained in greate detail below.\n",
+ "Each of these steps will be explained in great detail below.\n",
"\n",
"1. **Get Issues**- fetches issues from the repository.\n",
"\n",
- "2. **Get Issue**- feteches details about a specific issue.\n",
+ "2. **Get Issue**- fetches details about a specific issue.\n",
"\n",
"3. **Comment on Issue**- posts a comment on a specific issue.\n",
"\n",
diff --git a/docs/docs/integrations/toolkits/sql_database.ipynb b/docs/docs/integrations/toolkits/sql_database.ipynb
index eae793da1ac..76d50084ba1 100644
--- a/docs/docs/integrations/toolkits/sql_database.ipynb
+++ b/docs/docs/integrations/toolkits/sql_database.ipynb
@@ -111,7 +111,7 @@
"id": "54c01168",
"metadata": {},
"source": [
- "## Disclamer â ď¸\n",
+ "## Disclaimer â ď¸\n",
"\n",
"The query chain may generate insert/update/delete queries. When this is not expected, use a custom prompt or create a SQL users without write permissions.\n",
"\n",
diff --git a/docs/docs/integrations/vectorstores/analyticdb.ipynb b/docs/docs/integrations/vectorstores/analyticdb.ipynb
index 43fa2b14068..86894ce8dd6 100644
--- a/docs/docs/integrations/vectorstores/analyticdb.ipynb
+++ b/docs/docs/integrations/vectorstores/analyticdb.ipynb
@@ -8,7 +8,7 @@
"\n",
">[AnalyticDB for PostgreSQL](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.\n",
"\n",
- ">`AnalyticDB for PostgreSQL` is developed based on the open source `Greenplum Database` project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.\n",
+ ">`AnalyticDB for PostgreSQL` is developed based on the open-source `Greenplum Database` project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.\n",
"\n",
"This notebook shows how to use functionality related to the `AnalyticDB` vector database.\n",
"To run, you should have an [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) instance up and running:\n",
diff --git a/docs/docs/integrations/vectorstores/annoy.ipynb b/docs/docs/integrations/vectorstores/annoy.ipynb
index bf71d5bf2de..7f71d0c9e5d 100644
--- a/docs/docs/integrations/vectorstores/annoy.ipynb
+++ b/docs/docs/integrations/vectorstores/annoy.ipynb
@@ -18,7 +18,7 @@
"metadata": {},
"source": [
"```{note}\n",
- "NOTE: Annoy is read-only - once the index is built you cannot add any more emebddings!\n",
+ "NOTE: Annoy is read-only - once the index is built you cannot add any more embeddings!\n",
"If you want to progressively add new entries to your VectorStore then better choose an alternative!\n",
"```"
]
diff --git a/docs/docs/integrations/vectorstores/clarifai.ipynb b/docs/docs/integrations/vectorstores/clarifai.ipynb
index 02cafd01238..9c48f1917b6 100644
--- a/docs/docs/integrations/vectorstores/clarifai.ipynb
+++ b/docs/docs/integrations/vectorstores/clarifai.ipynb
@@ -276,7 +276,7 @@
"data": {
"text/plain": [
"[Document(page_content='And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at homeâthey have no serial numbers and canât be traced. \\n\\nAnd I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? \\n\\nBan assault weapons and high-capacity magazines. \\n\\nRepeal the liability shield that makes gun manufacturers the only industry in America that canât be sued. \\n\\nThese laws donât infringe on the Second Amendment. They save lives. \\n\\nThe most fundamental right in America is the right to vote â and to have it counted. And itâs under assault. \\n\\nIn state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \\n\\nWe cannot let this happen.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
- " Document(page_content='We canât change how divided weâve been. But we can change how we move forwardâon COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans whoâd grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nIâve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
+ " Document(page_content='We canât change how divided weâve been. But we can change how we move forwardâon COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans whoâd grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nIâve worked on these issues a long time. \\n\\nI know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
" Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâs been nominated, sheâs received a broad range of supportâfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, weâve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWeâve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWeâre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWeâre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
" Document(page_content='So letâs not abandon our streets. Or choose between safety and equal justice. \\n\\nLetâs come together to protect our communities, restore trust, and hold law enforcement accountable. \\n\\nThatâs why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. \\n\\nThatâs why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruptionâtrusted messengers breaking the cycle of violence and trauma and giving young people hope. \\n\\nWe should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. \\n\\nI ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.', metadata={'source': '../../../state_of_the_union.txt'})]"
]
diff --git a/docs/docs/integrations/vectorstores/hologres.ipynb b/docs/docs/integrations/vectorstores/hologres.ipynb
index 77ff7bf032e..de28c271363 100644
--- a/docs/docs/integrations/vectorstores/hologres.ipynb
+++ b/docs/docs/integrations/vectorstores/hologres.ipynb
@@ -10,7 +10,7 @@
">Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services. \n",
"\n",
">Hologres provides **vector database** functionality by adopting [Proxima](https://www.alibabacloud.com/help/en/hologres/latest/vector-processing).\n",
- ">Proxima is a high-performance software library developed by Alibaba DAMO Academy. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.\n",
+ ">Proxima is a high-performance software library developed by Alibaba DAMO Academy. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open-source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.\n",
"\n",
"This notebook shows how to use functionality related to the `Hologres Proxima` vector database.\n",
"Click [here](https://www.alibabacloud.com/zh/product/hologres) to fast deploy a Hologres cloud instance."
diff --git a/docs/docs/integrations/vectorstores/llm_rails.ipynb b/docs/docs/integrations/vectorstores/llm_rails.ipynb
index 0d6a1bc49b9..a703c2136de 100644
--- a/docs/docs/integrations/vectorstores/llm_rails.ipynb
+++ b/docs/docs/integrations/vectorstores/llm_rails.ipynb
@@ -11,7 +11,7 @@
"See the [LLMRails API documentation ](https://docs.llmrails.com/) for more information on how to use the API.\n",
"\n",
"This notebook shows how to use functionality related to the `LLMRails`'s integration with langchain.\n",
- "Note that unlike many other integrations in this category, LLMRails provides an end-to-end managed service for retrieval agumented generation, which includes:\n",
+ "Note that unlike many other integrations in this category, LLMRails provides an end-to-end managed service for retrieval augmented generation, which includes:\n",
"1. A way to extract text from document files and chunk them into sentences.\n",
"2. Its own embeddings model and vector store - each text segment is encoded into a vector embedding and stored in the LLMRails internal vector store\n",
"3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.llmrails.com/datastores/search))\n",
diff --git a/docs/docs/integrations/vectorstores/marqo.ipynb b/docs/docs/integrations/vectorstores/marqo.ipynb
index d208f7a6f74..01c88627ffa 100644
--- a/docs/docs/integrations/vectorstores/marqo.ipynb
+++ b/docs/docs/integrations/vectorstores/marqo.ipynb
@@ -10,7 +10,7 @@
"\n",
"This notebook shows how to use functionality related to the Marqo vectorstore.\n",
"\n",
- ">[Marqo](https://www.marqo.ai/) is an open-source vector search engine. Marqo allows you to store and query multimodal data such as text and images. Marqo creates the vectors for you using a huge selection of opensource models, you can also provide your own finetuned models and Marqo will handle the loading and inference for you.\n",
+ ">[Marqo](https://www.marqo.ai/) is an open-source vector search engine. Marqo allows you to store and query multi-modal data such as text and images. Marqo creates the vectors for you using a huge selection of open-source models, you can also provide your own fine-tuned models and Marqo will handle the loading and inference for you.\n",
"\n",
"To run this notebook with our docker image please run the following commands first to get Marqo:\n",
"\n",
diff --git a/docs/docs/integrations/vectorstores/myscale.ipynb b/docs/docs/integrations/vectorstores/myscale.ipynb
index 98fd3d14788..0eb98f88d91 100644
--- a/docs/docs/integrations/vectorstores/myscale.ipynb
+++ b/docs/docs/integrations/vectorstores/myscale.ipynb
@@ -19,7 +19,7 @@
"id": "43ead5d5-2c1f-4dce-a69a-cb00e4f9d6f0",
"metadata": {},
"source": [
- "## Setting up envrionments"
+ "## Setting up environments"
]
},
{
@@ -174,7 +174,7 @@
"\n",
"**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
"\n",
- "If you custimized your `column_map` under your setting, you search with filter like this:"
+ "If you customized your `column_map` under your setting, you search with filter like this:"
]
},
{
diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb
index 397758f216b..4e34060d3e0 100644
--- a/docs/docs/integrations/vectorstores/pgvector.ipynb
+++ b/docs/docs/integrations/vectorstores/pgvector.ipynb
@@ -344,7 +344,7 @@
"\n",
"Iâve worked on these issues a long time. \n",
"\n",
- "I know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.\n",
+ "I know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.\n",
"--------------------------------------------------------------------------------\n",
"--------------------------------------------------------------------------------\n",
"Score: 0.2448441215698569\n",
diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb
index c362639c84f..e82388ac391 100644
--- a/docs/docs/integrations/vectorstores/qdrant.ipynb
+++ b/docs/docs/integrations/vectorstores/qdrant.ipynb
@@ -497,7 +497,7 @@
"\n",
"I\u2019ve worked on these issues a long time. \n",
"\n",
- "I know what works: Investing in crime preventionand community police officers who\u2019ll walk the beat, who\u2019ll know the neighborhood, and who can restore trust and safety. \n",
+ "I know what works: Investing in crime prevention and community police officers who\u2019ll walk the beat, who\u2019ll know the neighborhood, and who can restore trust and safety. \n",
"\n"
]
}
diff --git a/docs/docs/integrations/vectorstores/sklearn.ipynb b/docs/docs/integrations/vectorstores/sklearn.ipynb
index b93c734a74f..ce397fa647d 100644
--- a/docs/docs/integrations/vectorstores/sklearn.ipynb
+++ b/docs/docs/integrations/vectorstores/sklearn.ipynb
@@ -6,7 +6,7 @@
"source": [
"# scikit-learn\n",
"\n",
- ">[scikit-learn](https://scikit-learn.org/stable/) is an open source collection of machine learning algorithms, including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.\n",
+ ">[scikit-learn](https://scikit-learn.org/stable/) is an open-source collection of machine learning algorithms, including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.\n",
"\n",
"This notebook shows how to use the `SKLearnVectorStore` vector database."
]
diff --git a/docs/docs/integrations/vectorstores/supabase.ipynb b/docs/docs/integrations/vectorstores/supabase.ipynb
index 961ac208dae..ad142aa9ffd 100644
--- a/docs/docs/integrations/vectorstores/supabase.ipynb
+++ b/docs/docs/integrations/vectorstores/supabase.ipynb
@@ -13,7 +13,7 @@
"id": "cc80fa84-1f2f-48b4-bd39-3e6412f012f1",
"metadata": {},
"source": [
- ">[Supabase](https://supabase.com/docs) is an open source Firebase alternative. `Supabase` is built on top of `PostgreSQL`, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.\n",
+ ">[Supabase](https://supabase.com/docs) is an open-source Firebase alternative. `Supabase` is built on top of `PostgreSQL`, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.\n",
"\n",
">[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.\n",
"\n",
@@ -437,7 +437,7 @@
"\n",
"Iâve worked on these issues a long time. \n",
"\n",
- "I know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.\n"
+ "I know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.\n"
]
}
],
diff --git a/docs/docs/integrations/vectorstores/tair.ipynb b/docs/docs/integrations/vectorstores/tair.ipynb
index 0b55b9f5611..8f0f6a22aa0 100644
--- a/docs/docs/integrations/vectorstores/tair.ipynb
+++ b/docs/docs/integrations/vectorstores/tair.ipynb
@@ -7,7 +7,7 @@
"# Tair\n",
"\n",
">[Tair](https://www.alibabacloud.com/help/en/tair/latest/what-is-tair) is a cloud native in-memory database service developed by `Alibaba Cloud`. \n",
- "It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open source `Redis`. `Tair` also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.\n",
+ "It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open-source `Redis`. `Tair` also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.\n",
"\n",
"This notebook shows how to use functionality related to the `Tair` vector database.\n",
"\n",
diff --git a/docs/docs/integrations/vectorstores/tigris.ipynb b/docs/docs/integrations/vectorstores/tigris.ipynb
index ba529c1033b..c2119ac4704 100644
--- a/docs/docs/integrations/vectorstores/tigris.ipynb
+++ b/docs/docs/integrations/vectorstores/tigris.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Tigris\n",
"\n",
- "> [Tigris](htttps://tigrisdata.com) is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.\n",
+ "> [Tigris](htttps://tigrisdata.com) is an open-source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.\n",
"> `Tigris` eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead."
]
},
diff --git a/docs/docs/integrations/vectorstores/typesense.ipynb b/docs/docs/integrations/vectorstores/typesense.ipynb
index a547f5c640f..2daf5cf160c 100644
--- a/docs/docs/integrations/vectorstores/typesense.ipynb
+++ b/docs/docs/integrations/vectorstores/typesense.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Typesense\n",
"\n",
- "> [Typesense](https://typesense.org) is an open source, in-memory search engine, that you can either [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run on [Typesense Cloud](https://cloud.typesense.org/).\n",
+ "> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run on [Typesense Cloud](https://cloud.typesense.org/).\n",
">\n",
"> Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.\n",
">\n",
diff --git a/docs/docs/integrations/vectorstores/vectara.ipynb b/docs/docs/integrations/vectorstores/vectara.ipynb
index e95504860bd..907588a6dd4 100644
--- a/docs/docs/integrations/vectorstores/vectara.ipynb
+++ b/docs/docs/integrations/vectorstores/vectara.ipynb
@@ -11,7 +11,7 @@
"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
"\n",
"This notebook shows how to use functionality related to the `Vectara`'s integration with langchain.\n",
- "Note that unlike many other integrations in this category, Vectara provides an end-to-end managed service for [Grounded Generation](https://vectara.com/grounded-generation/) (aka retrieval agumented generation), which includes:\n",
+ "Note that unlike many other integrations in this category, Vectara provides an end-to-end managed service for [Grounded Generation](https://vectara.com/grounded-generation/) (aka retrieval augmented generation), which includes:\n",
"1. A way to extract text from document files and chunk them into sentences.\n",
"2. Its own embeddings model and vector store - each text segment is encoded into a vector embedding and stored in the Vectara internal vector store\n",
"3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching))\n",
diff --git a/docs/docs/integrations/vectorstores/zep.ipynb b/docs/docs/integrations/vectorstores/zep.ipynb
index 4456aece0be..b8a3e5fdfb8 100644
--- a/docs/docs/integrations/vectorstores/zep.ipynb
+++ b/docs/docs/integrations/vectorstores/zep.ipynb
@@ -5,7 +5,7 @@
"source": [
"# Zep\n",
"\n",
- "Zep is an open source long-term memory store for LLM applications. Zep makes it easy to add relevant documents,\n",
+ "Zep is an open-source long-term memory store for LLM applications. Zep makes it easy to add relevant documents,\n",
"chat history memory & rich user data to your LLM app's prompts.\n",
"\n",
"**Note:** The `ZepVectorStore` works with `Documents` and is intended to be used as a `Retriever`.\n",
diff --git a/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb b/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb
index ece210c8415..2d0d4ed2d26 100644
--- a/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb
+++ b/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb
@@ -24,7 +24,9 @@
"source": [
"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
"from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
- "from langchain.llms import OpenAI\nfrom langchain.chains import LLMChain\nfrom langchain.prompts import PromptTemplate\n",
+ "from langchain.llms import OpenAI\n",
+ "from langchain.chains import LLMChain\n",
+ "from langchain.prompts import PromptTemplate\n",
"from langchain.utilities import GoogleSearchAPIWrapper"
]
},
@@ -45,7 +47,7 @@
"prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
"readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
- "summry_chain = LLMChain(\n",
+ "summary_chain = LLMChain(\n",
" llm=OpenAI(),\n",
" prompt=prompt,\n",
" verbose=True,\n",
@@ -69,7 +71,7 @@
" ),\n",
" Tool(\n",
" name=\"Summary\",\n",
- " func=summry_chain.run,\n",
+ " func=summary_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",
"]"
@@ -317,7 +319,7 @@
"\n",
"prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
- "summry_chain = LLMChain(\n",
+ "summary_chain = LLMChain(\n",
" llm=OpenAI(),\n",
" prompt=prompt,\n",
" verbose=True,\n",
@@ -333,7 +335,7 @@
" ),\n",
" Tool(\n",
" name=\"Summary\",\n",
- " func=summry_chain.run,\n",
+ " func=summary_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",
diff --git a/docs/docs/modules/agents/tools/multi_input_tool.ipynb b/docs/docs/modules/agents/tools/multi_input_tool.ipynb
index 8ea3bb639a0..15376d0d985 100644
--- a/docs/docs/modules/agents/tools/multi_input_tool.ipynb
+++ b/docs/docs/modules/agents/tools/multi_input_tool.ipynb
@@ -139,7 +139,7 @@
"source": [
"## Multi-Input Tools with a string format\n",
"\n",
- "An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliably generate structured schema. \n",
+ "An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relevant values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliably generate structured schema. \n",
"\n",
"Let's take the multiplication function as an example. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
]
diff --git a/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb b/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb
index 86aadd97c7e..11b21c6af22 100644
--- a/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb
+++ b/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb
@@ -45,7 +45,7 @@
"source": [
"#### Simple usage\n",
"\n",
- "Specify the LLM to use for query generation, and the retriver will do the rest."
+ "Specify the LLM to use for query generation, and the retriever will do the rest."
]
},
{
@@ -155,7 +155,7 @@
" different versions of the given user question to retrieve relevant documents from a vector \n",
" database. By generating multiple perspectives on the user question, your goal is to help\n",
" the user overcome some of the limitations of the distance-based similarity search. \n",
- " Provide these alternative questions seperated by newlines.\n",
+ " Provide these alternative questions separated by newlines.\n",
" Original question: {question}\"\"\",\n",
")\n",
"llm = ChatOpenAI(temperature=0)\n",
diff --git a/docs/docs/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb b/docs/docs/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb
index d437d95f53d..0c02dbfcc68 100644
--- a/docs/docs/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb
+++ b/docs/docs/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb
@@ -47,7 +47,7 @@
"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
"metadata": {},
"source": [
- "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get an OpenAI API Key for valid accesss to LLMs."
+ "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get an OpenAI API Key for valid access to LLMs."
]
},
{
diff --git a/docs/docs/modules/data_connection/retrievers/web_research.ipynb b/docs/docs/modules/data_connection/retrievers/web_research.ipynb
index f880266a0f2..d016e8e4d40 100644
--- a/docs/docs/modules/data_connection/retrievers/web_research.ipynb
+++ b/docs/docs/modules/data_connection/retrievers/web_research.ipynb
@@ -144,14 +144,14 @@
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How do LLM powered autonomous agents utilize task decomposition?\\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '3. What role does task decomposition play in the functioning of LLM powered autonomous agents?\\n', '4. Why is task decomposition important for LLM powered autonomous agents?\\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How do LLM powered autonomous agents utilize task decomposition?\\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '3. What role does task decomposition play in the functioning of LLM powered autonomous agents?\\n', '4. Why is task decomposition important for LLM powered autonomous agents?\\n']\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... In a LLM-powered autonomous agent system, LLM functions as the ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: []\n"
]
@@ -271,14 +271,14 @@
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How do LLM powered autonomous agents use task decomposition?\\n', '2. Why is task decomposition important for LLM powered autonomous agents?\\n', '3. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '4. What are the benefits of task decomposition in LLM powered autonomous agents?\\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How do LLM powered autonomous agents use task decomposition?\\n', '2. Why is task decomposition important for LLM powered autonomous agents?\\n', '3. Can you explain the concept of task decomposition in LLM powered autonomous agents?\\n', '4. What are the benefits of task decomposition in LLM powered autonomous agents?\\n']\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']\n",
"INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...\n",
@@ -490,14 +490,14 @@
"llama_print_timings: total time = 16236.13 ms\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \\n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \\n', '3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents? \\n', '4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents? \\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \\n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \\n', '3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents? \\n', '4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents? \\n']\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2)\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... A complicated task usually involves many steps. An agent needs to know what they are and plan ahead. Task Decomposition#. Chain of thought (CoT;\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']\n",
"INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...\n",
diff --git a/docs/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store.ipynb b/docs/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store.ipynb
index ec5e681f6c5..24610e16de8 100644
--- a/docs/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store.ipynb
+++ b/docs/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store.ipynb
@@ -23,7 +23,7 @@
"source": [
"## Feast\n",
"\n",
- "To start, we will use the popular open source feature store framework [Feast](https://github.com/feast-dev/feast).\n",
+ "To start, we will use the popular open-source feature store framework [Feast](https://github.com/feast-dev/feast).\n",
"\n",
"This assumes you have already run the steps in the README around getting started. We will build off of that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics."
]
@@ -220,7 +220,7 @@
"source": [
"## Tecton\n",
"\n",
- "Above, we showed how you could use Feast, a popular open source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs."
+ "Above, we showed how you could use Feast, a popular open-source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs."
]
},
{
diff --git a/docs/docs/modules/model_io/prompts/prompt_templates/prompt_serialization.ipynb b/docs/docs/modules/model_io/prompts/prompt_templates/prompt_serialization.ipynb
index 28bc342fe41..0d0f12f90a4 100644
--- a/docs/docs/modules/model_io/prompts/prompt_templates/prompt_serialization.ipynb
+++ b/docs/docs/modules/model_io/prompts/prompt_templates/prompt_serialization.ipynb
@@ -7,13 +7,13 @@
"source": [
"# Serialization\n",
"\n",
- "It is often preferrable to store prompts not as python code but as files. This can make it easy to share, store, and version prompts. This notebook covers how to do that in LangChain, walking through all the different types of prompts and the different serialization options.\n",
+ "It is often preferable to store prompts not as python code but as files. This can make it easy to share, store, and version prompts. This notebook covers how to do that in LangChain, walking through all the different types of prompts and the different serialization options.\n",
"\n",
"At a high level, the following design principles are applied to serialization:\n",
"\n",
"1. Both JSON and YAML are supported. We want to support serialization methods that are human readable on disk, and YAML and JSON are two of the most popular methods for that. Note that this rule applies to prompts. For other assets, like examples, different serialization methods may be supported.\n",
"\n",
- "2. We support specifying everything in one file, or storing different components (templates, examples, etc) in different files and referencing them. For some cases, storing everything in file makes the most sense, but for others it is preferrable to split up some of the assets (long templates, large examples, reusable components). LangChain supports both.\n",
+ "2. We support specifying everything in one file, or storing different components (templates, examples, etc) in different files and referencing them. For some cases, storing everything in file makes the most sense, but for others it is preferable to split up some of the assets (long templates, large examples, reusable components). LangChain supports both.\n",
"\n",
"There is also a single entry point to load prompts from disk, making it easy to load any type of prompt."
]
diff --git a/docs/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining.ipynb b/docs/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining.ipynb
index 74316f704f2..c2233c46297 100644
--- a/docs/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining.ipynb
+++ b/docs/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining.ipynb
@@ -17,7 +17,7 @@
"source": [
"## String prompt pipelining\n",
"\n",
- "When working with string prompts, each template is joined togther. You can work with either prompts directly or strings (the first element in the list needs to be a prompt)."
+ "When working with string prompts, each template is joined together. You can work with either prompts directly or strings (the first element in the list needs to be a prompt)."
]
},
{
diff --git a/docs/docs/modules/paul_graham_essay.txt b/docs/docs/modules/paul_graham_essay.txt
index b572cb6726b..0bce3830b99 100644
--- a/docs/docs/modules/paul_graham_essay.txt
+++ b/docs/docs/modules/paul_graham_essay.txt
@@ -172,15 +172,15 @@ Around this time, in the spring of 2000, I had an idea. It was clear from our ex
I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.
-Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did.
+Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open-source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did.
By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra.
-I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company â especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open source project.
+I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company â especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open-source project.
Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.
-The subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.
+The subset I would build as an open-source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.
The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]
diff --git a/docs/docs/modules/state_of_the_union.txt b/docs/docs/modules/state_of_the_union.txt
index d50175de40e..b453aacdae3 100644
--- a/docs/docs/modules/state_of_the_union.txt
+++ b/docs/docs/modules/state_of_the_union.txt
@@ -495,7 +495,7 @@ I spoke with their families and told them that we are forever in debt for their
Iâve worked on these issues a long time.
-I know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.
+I know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.
So letâs not abandon our streets. Or choose between safety and equal justice.
diff --git a/docs/docs/use_cases/apis.ipynb b/docs/docs/use_cases/apis.ipynb
index 789e01e4e36..0072909e580 100644
--- a/docs/docs/use_cases/apis.ipynb
+++ b/docs/docs/use_cases/apis.ipynb
@@ -16,7 +16,7 @@
"id": "a15e6a18",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/apis.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/apis.ipynb)\n",
"\n",
"## Use case \n",
"\n",
diff --git a/docs/docs/use_cases/chatbots.ipynb b/docs/docs/use_cases/chatbots.ipynb
index cfad32fc815..0af38db7e6d 100644
--- a/docs/docs/use_cases/chatbots.ipynb
+++ b/docs/docs/use_cases/chatbots.ipynb
@@ -16,7 +16,7 @@
"id": "ee7f95e4",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/chatbots.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/chatbots.ipynb)\n",
"\n",
"## Use case\n",
"\n",
diff --git a/docs/docs/use_cases/data_generation.ipynb b/docs/docs/use_cases/data_generation.ipynb
index 5b862108912..9b543d1a039 100644
--- a/docs/docs/use_cases/data_generation.ipynb
+++ b/docs/docs/use_cases/data_generation.ipynb
@@ -17,7 +17,7 @@
"id": "aa3571cc",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/data_generation.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/data_generation.ipynb)\n",
"\n",
"## Use case\n",
"\n",
diff --git a/docs/docs/use_cases/extraction.ipynb b/docs/docs/use_cases/extraction.ipynb
index 6fe7d0fc92e..9403afd6c2d 100644
--- a/docs/docs/use_cases/extraction.ipynb
+++ b/docs/docs/use_cases/extraction.ipynb
@@ -576,7 +576,7 @@
"\n",
"### Going deeper\n",
"\n",
- "* The [output parser](/docs/modules/model_io/output_parsers/) documentation includes various parser examples for specific types (e.g., lists, datetimne, enum, etc). \n",
+ "* The [output parser](/docs/modules/model_io/output_parsers/) documentation includes various parser examples for specific types (e.g., lists, datetime, enum, etc). \n",
"* [JSONFormer](/docs/integrations/llms/jsonformer_experimental) offers another way for structured decoding of a subset of the JSON Schema.\n",
"* [Kor](https://eyurtsev.github.io/kor/) is another library for extraction where schema and examples can be provided to the LLM."
]
diff --git a/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb b/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb
index 7a63f78888d..30144d0330f 100644
--- a/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb
+++ b/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Diffbot Graph Transformer\n",
"\n",
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/graph/diffbot_graphtransformer.ipynb)\n",
"\n",
"## Use case\n",
"\n",
diff --git a/docs/docs/use_cases/graph/graph_arangodb_qa.ipynb b/docs/docs/use_cases/graph/graph_arangodb_qa.ipynb
index e82da530ffc..411a2ef0568 100644
--- a/docs/docs/use_cases/graph/graph_arangodb_qa.ipynb
+++ b/docs/docs/use_cases/graph/graph_arangodb_qa.ipynb
@@ -9,7 +9,7 @@
"source": [
"# ArangoDB QA chain\n",
"\n",
- "[](https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Langchain.ipynb)\n",
+ "[](https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Langchain.ipynb)\n",
"\n",
"This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
]
diff --git a/docs/docs/use_cases/qa_structured/sql.ipynb b/docs/docs/use_cases/qa_structured/sql.ipynb
index a3f8e375003..032b4eefb4a 100644
--- a/docs/docs/use_cases/qa_structured/sql.ipynb
+++ b/docs/docs/use_cases/qa_structured/sql.ipynb
@@ -14,13 +14,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/qa_structured/sql.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/qa_structured/sql.ipynb)\n",
"\n",
"## Use case\n",
"\n",
"Enterprise data is often stored in SQL databases.\n",
"\n",
- "LLMs make it possible to interact with SQL databases using natural langugae.\n",
+ "LLMs make it possible to interact with SQL databases using natural language.\n",
"\n",
"LangChain offers SQL Chains and Agents to build and run SQL queries based on natural language prompts. \n",
"\n",
@@ -275,7 +275,7 @@
"\n",
"{table_info}.\n",
"\n",
- "Some examples of SQL queries that corrsespond to questions are:\n",
+ "Some examples of SQL queries that correspond to questions are:\n",
"\n",
"{few_shot_examples}\n",
"\n",
@@ -950,7 +950,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we can proceed with creating the custom **retreiver tool** and the final agent:"
+ "Now we can proceed with creating the custom **retriever tool** and the final agent:"
]
},
{
diff --git a/docs/docs/use_cases/question_answering/code_understanding.ipynb b/docs/docs/use_cases/question_answering/code_understanding.ipynb
index 9bdf9c49730..500d4177d2b 100644
--- a/docs/docs/use_cases/question_answering/code_understanding.ipynb
+++ b/docs/docs/use_cases/question_answering/code_understanding.ipynb
@@ -57,7 +57,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We'lll follow the structure of [this notebook](https://github.com/cristobalcl/LearningLangChain/blob/master/notebooks/04%20-%20QA%20with%20code.ipynb) and employ [context aware code splitting](https://python.langchain.com/docs/integrations/document_loaders/source_code)."
+ "We'll follow the structure of [this notebook](https://github.com/cristobalcl/LearningLangChain/blob/master/notebooks/04%20-%20QA%20with%20code.ipynb) and employ [context aware code splitting](https://python.langchain.com/docs/integrations/document_loaders/source_code)."
]
},
{
@@ -331,7 +331,7 @@
"source": [
"The can look at the [LangSmith trace](https://smith.langchain.com/public/2b23045f-4e49-4d2d-8980-dec85259af36/r) to see what is happening under the hood:\n",
"\n",
- "* In particular, the code well structured and kept together in the retrival output\n",
+ "* In particular, the code well structured and kept together in the retrieval output\n",
"* The retrieved code and chat history are passed to the LLM for answer distillation\n",
"\n",
""
diff --git a/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb b/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb
index ad8f202e77d..6f1e6615a6b 100644
--- a/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb
+++ b/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb
@@ -269,7 +269,7 @@
"Invoking: `search_state_of_union` with `{'query': 'Kentaji Brown Jackson'}`\n",
"\n",
"\n",
- "\u001b[0m\u001b[36;1m\u001b[1;3m[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while youâre at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, Iâd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyerâan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nationâs top legal minds, who will continue Justice Breyerâs legacy of excellence.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='One was stationed at bases and breathing in toxic smoke from âburn pitsâ that incinerated wastes of warâmedical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the worldâs fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe donât know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut Iâm committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson. \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeathâs widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâs been nominated, sheâs received a broad range of supportâfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, weâve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWeâve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWeâre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWeâre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='We canât change how divided weâve been. But we can change how we move forwardâon COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans whoâd grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nIâve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'})]\u001b[0m\u001b[32;1m\u001b[1;3mIn the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+ "\u001b[0m\u001b[36;1m\u001b[1;3m[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while youâre at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, Iâd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyerâan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nationâs top legal minds, who will continue Justice Breyerâs legacy of excellence.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='One was stationed at bases and breathing in toxic smoke from âburn pitsâ that incinerated wastes of warâmedical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the worldâs fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe donât know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut Iâm committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson. \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeathâs widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since sheâs been nominated, sheâs received a broad range of supportâfrom the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, weâve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWeâve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWeâre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWeâre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'}), Document(page_content='We canât change how divided weâve been. But we can change how we move forwardâon COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans whoâd grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nIâve worked on these issues a long time. \\n\\nI know what works: Investing in crime prevention and community police officers whoâll walk the beat, whoâll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../../../docs/docs/modules/state_of_the_union.txt'})]\u001b[0m\u001b[32;1m\u001b[1;3mIn the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
diff --git a/docs/docs/use_cases/question_answering/document-context-aware-QA.ipynb b/docs/docs/use_cases/question_answering/document-context-aware-QA.ipynb
index 54fe471375c..f2237c94581 100644
--- a/docs/docs/use_cases/question_answering/document-context-aware-QA.ipynb
+++ b/docs/docs/use_cases/question_answering/document-context-aware-QA.ipynb
@@ -94,7 +94,7 @@
"source": [
"This sets us up well do perform metadata filtering based on the document structure.\n",
"\n",
- "Let's bring this all togther by building a vectorstore first."
+ "Let's bring this all together by building a vectorstore first."
]
},
{
@@ -151,7 +151,7 @@
"]\n",
"document_content_description = \"Major sections of the document\"\n",
"\n",
- "# Define self query retriver\n",
+ "# Define self query retriever\n",
"llm = OpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(\n",
" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
@@ -272,7 +272,7 @@
"id": "1af7720f",
"metadata": {},
"source": [
- "Now, we can create chat or Q+A apps that are aware of the explict document structure. \n",
+ "Now, we can create chat or Q+A apps that are aware of the explicit document structure. \n",
"\n",
"The ability to retain document structure for metadata filtering can be helpful for complicated or longer documents."
]
diff --git a/docs/docs/use_cases/question_answering/index.ipynb b/docs/docs/use_cases/question_answering/index.ipynb
index f27abd5102c..4669f690617 100644
--- a/docs/docs/use_cases/question_answering/index.ipynb
+++ b/docs/docs/use_cases/question_answering/index.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Retrieval-augmented generation (RAG)\n",
"\n",
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/question_answering/qa.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/question_answering/qa.ipynb)\n",
"\n",
"## Use case\n",
"Suppose you have some text documents (PDF, blog, Notion pages, etc.) and want to ask questions related to the contents of those documents. \n",
diff --git a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb
index 9f4b8a816ac..f2139ff696a 100644
--- a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb
+++ b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb
@@ -9,7 +9,7 @@
"\n",
"The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the importance of running LLMs locally.\n",
"\n",
- "LangChain has [integrations](https://integrations.langchain.com/) with many open source LLMs that can be run locally.\n",
+ "LangChain has [integrations](https://integrations.langchain.com/) with many open-source LLMs that can be run locally.\n",
"\n",
"See [here](docs/guides/local_llms) for setup instructions for these LLMs. \n",
"\n",
@@ -35,7 +35,7 @@
"id": "5e7543fa",
"metadata": {},
"source": [
- "Load and split an example docucment.\n",
+ "Load and split an example document.\n",
"\n",
"We'll use a blog post on agents as an example."
]
diff --git a/docs/docs/use_cases/question_answering/multiple_retrieval.ipynb b/docs/docs/use_cases/question_answering/multiple_retrieval.ipynb
index 7d197acb673..8847a2b79e8 100644
--- a/docs/docs/use_cases/question_answering/multiple_retrieval.ipynb
+++ b/docs/docs/use_cases/question_answering/multiple_retrieval.ipynb
@@ -9,7 +9,7 @@
"\n",
"Often times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different databases altogether!\n",
"\n",
- "A key part is is doing as much of the retrieval in parrelel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parrellism out of the box.\n",
+ "A key part is is doing as much of the retrieval in parallel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parallelism out of the box.\n",
"\n",
"Let's take a look where we do retrieval over a SQL database and a vectorstore."
]
diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb
index 01bf1b23314..fd22cdc9b04 100644
--- a/docs/docs/use_cases/summarization.ipynb
+++ b/docs/docs/use_cases/summarization.ipynb
@@ -16,7 +16,7 @@
"id": "cf13f702",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/summarization.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/summarization.ipynb)\n",
"\n",
"## Use case\n",
"\n",
diff --git a/docs/docs/use_cases/tagging.ipynb b/docs/docs/use_cases/tagging.ipynb
index f24ec43e38c..7b6b5280d9f 100644
--- a/docs/docs/use_cases/tagging.ipynb
+++ b/docs/docs/use_cases/tagging.ipynb
@@ -16,7 +16,7 @@
"id": "a0507a4b",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/tagging.ipynb)\n",
+ "[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/tagging.ipynb)\n",
"\n",
"## Use case\n",
"\n",
diff --git a/docs/docs/use_cases/web_scraping.ipynb b/docs/docs/use_cases/web_scraping.ipynb
index fc01c3ecfa8..5802eec8fc6 100644
--- a/docs/docs/use_cases/web_scraping.ipynb
+++ b/docs/docs/use_cases/web_scraping.ipynb
@@ -537,10 +537,10 @@
"INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'How do LLM Powered Autonomous Agents work?', 'text': LineList(lines=['1. What is the functioning principle of LLM Powered Autonomous Agents?\\n', '2. How do LLM Powered Autonomous Agents operate?\\n'])}\n",
"INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. What is the functioning principle of LLM Powered Autonomous Agents?\\n', '2. How do LLM Powered Autonomous Agents operate?\\n']\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': 'LLM Powered Autonomous Agents | Hacker News', 'link': 'https://news.ycombinator.com/item?id=36488871', 'snippet': 'Jun 26, 2023 ... Exactly. A temperature of 0 means you always pick the highest probability token (i.e. the \"max\" function), while a temperature of 1 means you\\xa0...'}]\n",
- "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+ "INFO:langchain.retrievers.web_research:Searching for relevant urls ...\n",
"INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2) by\\xa0...'}]\n",
"INFO:langchain.retrievers.web_research:New URLs to load: []\n",
"INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls...\n"
@@ -577,7 +577,7 @@
"source": [
"### Going deeper \n",
"\n",
- "* Here's a [app](https://github.com/langchain-ai/web-explorer/tree/main) that wraps this retriver with a lighweight UI."
+ "* Here's a [app](https://github.com/langchain-ai/web-explorer/tree/main) that wraps this retriever with a lighweight UI."
],
"id": "7a940df1"
},
diff --git a/docs/snippets/modules/agents/agent_types/structured_chat.mdx b/docs/snippets/modules/agents/agent_types/structured_chat.mdx
index e7b7d6e5dea..6abbac5c4db 100644
--- a/docs/snippets/modules/agents/agent_types/structured_chat.mdx
+++ b/docs/snippets/modules/agents/agent_types/structured_chat.mdx
@@ -126,7 +126,7 @@ print(response)
TL;DR
- We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applications, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.
+ We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open-source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applications, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.
đĄ TL;DR: Weâve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin
diff --git a/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx b/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx
index 2087fc9e4ca..008b161c0aa 100644
--- a/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx
+++ b/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx
@@ -158,7 +158,7 @@ markdown_text = """
pip install langchain
```
-As an open source project in a rapidly developing field, we are extremely open to contributions.
+As an open-source project in a rapidly developing field, we are extremely open to contributions.
"""
````
@@ -180,7 +180,7 @@ md_docs
Document(page_content="```bash\n# Hopefully this code block isn't split", metadata={}),
Document(page_content='pip install langchain', metadata={}),
Document(page_content='```', metadata={}),
- Document(page_content='As an open source project in a rapidly developing field, we', metadata={}),
+ Document(page_content='As an open-source project in a rapidly developing field, we', metadata={}),
Document(page_content='are extremely open to contributions.', metadata={})]
```
@@ -276,7 +276,7 @@ html_text = """
⥠Building applications with LLMs through composability âĄ
- As an open source project in a rapidly developing field, we are extremely open to contributions.
+ As an open-source project in a rapidly developing field, we are extremely open to contributions.