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Julien Salinas
2023-08-23 14:42:40 +02:00
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4
docs/api_reference/guide_imports.json
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@@ -2071,8 +2071,8 @@
"PromptLayer": "https://python.langchain.com/docs/integrations/providers/promptlayer",
"PromptLayer OpenAI": "https://python.langchain.com/docs/integrations/llms/promptlayer_openai"
},
"DeepLake": {
"Deep Lake": "https://python.langchain.com/docs/integrations/providers/deeplake",
"Activeloop DeepLake": {
"Deep Lake": "https://python.langchain.com/docs/integrations/providers/activeloop_deeplake",
"Activeloop's Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake",
"Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake",
"Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake",

2
docs/docs_skeleton/vercel.json
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@@ -166,7 +166,7 @@
},
{
"source": "/docs/integrations/deeplake",
"destination": "/docs/integrations/providers/deeplake"
"destination": "/docs/integrations/providers/activeloop_deeplake"
},
{
"source": "/docs/integrations/diffbot",

4
docs/extras/guides/deployments/template_repos.mdx
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@@ -79,3 +79,7 @@ See OpenLLM's [integration doc](https://github.com/bentoml/OpenLLM#%EF%B8%8F-int
## [Databutton](https://databutton.com/home?new-data-app=true)
These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.
## [AzureML Online Endpoint](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/llm/langchain/1_langchain_basic_deploy.ipynb)
A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint.

39
docs/extras/integrations/chat/openai.ipynb
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@@ -143,12 +143,39 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c095285d",
"cell_type": "markdown",
"id": "57e27714",
"metadata": {},
"outputs": [],
"source": []
"source": [
"## Fine-tuning\n",
"\n",
"You can call fine-tuned OpenAI models by passing in your corresponding `modelName` parameter.\n",
"\n",
"This generally takes the form of `ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}`. For example:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "33c4a8b0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fine_tuned_model = ChatOpenAI(temperature=0, model_name=\"ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR\")\n",
"\n",
"fine_tuned_model(messages)"
]
}
],
"metadata": {
@@ -167,7 +194,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.10.5"
}
},
"nbformat": 4,

225
docs/extras/integrations/document_loaders/polars_dataframe.ipynb Normal file
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@@ -0,0 +1,225 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "213a38a2",
"metadata": {},
"source": [
"# Polars DataFrame\n",
"\n",
"This notebook goes over how to load data from a [polars](https://pola-rs.github.io/polars-book/user-guide/) DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f6a7a9e4-80d6-486a-b2e3-636c568aa97c",
"metadata": {},
"outputs": [],
"source": [
"#!pip install polars"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "79331964",
"metadata": {},
"outputs": [],
"source": [
"import polars as pl"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e487044c",
"metadata": {},
"outputs": [],
"source": [
"df = pl.read_csv(\"example_data/mlb_teams_2012.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ac273ca1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr > th,\n",
".dataframe > tbody > tr > td {\n",
" text-align: right;\n",
"}\n",
"</style>\n",
"<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Team</th><th> &quot;Payroll (millions)&quot;</th><th> &quot;Wins&quot;</th></tr><tr><td>str</td><td>f64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Nationals&quot;</td><td>81.34</td><td>98</td></tr><tr><td>&quot;Reds&quot;</td><td>82.2</td><td>97</td></tr><tr><td>&quot;Yankees&quot;</td><td>197.96</td><td>95</td></tr><tr><td>&quot;Giants&quot;</td><td>117.62</td><td>94</td></tr><tr><td>&quot;Braves&quot;</td><td>83.31</td><td>94</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (5, 3)\n",
"┌───────────┬───────────────────────┬─────────┐\n",
"│ Team ┆ \"Payroll (millions)\" ┆ \"Wins\" │\n",
"│ --- ┆ --- ┆ --- │\n",
"│ str ┆ f64 ┆ i64 │\n",
"╞═══════════╪═══════════════════════╪═════════╡\n",
"│ Nationals ┆ 81.34 ┆ 98 │\n",
"│ Reds ┆ 82.2 ┆ 97 │\n",
"│ Yankees ┆ 197.96 ┆ 95 │\n",
"│ Giants ┆ 117.62 ┆ 94 │\n",
"│ Braves ┆ 83.31 ┆ 94 │\n",
"└───────────┴───────────────────────┴─────────┘"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "66e47a13",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import PolarsDataFrameLoader"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2334caca",
"metadata": {},
"outputs": [],
"source": [
"loader = PolarsDataFrameLoader(df, page_content_column=\"Team\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d616c2b0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}),\n",
" Document(page_content='Reds', metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}),\n",
" Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}),\n",
" Document(page_content='Giants', metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}),\n",
" Document(page_content='Braves', metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}),\n",
" Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}),\n",
" Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}),\n",
" Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}),\n",
" Document(page_content='Rays', metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}),\n",
" Document(page_content='Angels', metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}),\n",
" Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}),\n",
" Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}),\n",
" Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}),\n",
" Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}),\n",
" Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}),\n",
" Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}),\n",
" Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}),\n",
" Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}),\n",
" Document(page_content='Padres', metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}),\n",
" Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}),\n",
" Document(page_content='Mets', metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}),\n",
" Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}),\n",
" Document(page_content='Royals', metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}),\n",
" Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}),\n",
" Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}),\n",
" Document(page_content='Indians', metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}),\n",
" Document(page_content='Twins', metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}),\n",
" Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}),\n",
" Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}),\n",
" Document(page_content='Astros', metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55})]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "beb55c2f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='Nationals' metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}\n",
"page_content='Reds' metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}\n",
"page_content='Yankees' metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}\n",
"page_content='Giants' metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}\n",
"page_content='Braves' metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}\n",
"page_content='Athletics' metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}\n",
"page_content='Rangers' metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}\n",
"page_content='Orioles' metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}\n",
"page_content='Rays' metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}\n",
"page_content='Angels' metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}\n",
"page_content='Tigers' metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}\n",
"page_content='Cardinals' metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}\n",
"page_content='Dodgers' metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}\n",
"page_content='White Sox' metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}\n",
"page_content='Brewers' metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}\n",
"page_content='Phillies' metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}\n",
"page_content='Diamondbacks' metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}\n",
"page_content='Pirates' metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}\n",
"page_content='Padres' metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}\n",
"page_content='Mariners' metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}\n",
"page_content='Mets' metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}\n",
"page_content='Blue Jays' metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}\n",
"page_content='Royals' metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}\n",
"page_content='Marlins' metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}\n",
"page_content='Red Sox' metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}\n",
"page_content='Indians' metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}\n",
"page_content='Twins' metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}\n",
"page_content='Rockies' metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}\n",
"page_content='Cubs' metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}\n",
"page_content='Astros' metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55}\n"
]
}
],
"source": [
"# Use lazy load for larger table, which won't read the full table into memory\n",
"for i in loader.lazy_load():\n",
" print(i)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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docs/extras/integrations/providers/deeplake.mdx → docs/extras/integrations/providers/activeloop_deeplake.mdx
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@@ -1,4 +1,4 @@
# Deep Lake
# Activeloop Deep Lake
This page covers how to use the Deep Lake ecosystem within LangChain.
## Why Deep Lake?
@@ -6,9 +6,15 @@ This page covers how to use the Deep Lake ecosystem within LangChain.
- Not only stores embeddings, but also the original data with automatic version control.
- Truly serverless. Doesn't require another service and can be used with major cloud providers (AWS S3, GCS, etc.)
Activeloop Deep Lake supports SelfQuery Retrieval:
[Activeloop Deep Lake Self Query Retrieval](/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query)
## More Resources
1. [Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/)
2. [Twitter the-algorithm codebase analysis with Deep Lake](../use_cases/code/twitter-the-algorithm-analysis-deeplake.html)
2. [Twitter the-algorithm codebase analysis with Deep Lake](/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake)
4. [Code Understanding](/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query)
3. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
4. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Get started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
@@ -27,4 +33,4 @@ from langchain.vectorstores import DeepLake
```
For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](/docs/integrations/vectorstores/deeplake.html)
For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](/docs/integrations/vectorstores/activeloop_deeplake)

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docs/extras/integrations/providers/atlas.mdx
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@@ -1,27 +1,19 @@
# AtlasDB
# Atlas
>[Nomic Atlas](https://docs.nomic.ai/index.html) is a platform for interacting with both
> small and internet scale unstructured datasets.
This page covers how to use Nomic's Atlas ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
## Installation and Setup
- Install the Python package with `pip install nomic`
- Nomic is also included in langchains poetry extras `poetry install -E all`
## Wrappers
### VectorStore
There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
Please see [the Atlas docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
- `Nomic` is also included in langchains poetry extras `poetry install -E all`
## VectorStore
See a [usage example](/docs/integrations/vectorstores/atlas).
To import this vectorstore:
```python
from langchain.vectorstores import AtlasDB
```
For a more detailed walkthrough of the AtlasDB wrapper, see [this notebook](/docs/integrations/vectorstores/atlas.html)
```

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docs/extras/integrations/providers/clickhouse.mdx Normal file
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@@ -0,0 +1,25 @@
# ClickHouse
> [ClickHouse](https://clickhouse.com/) is the fast and resource efficient open-source database for real-time
> apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries.
> It has data structures and distance search functions (like `L2Distance`) as well as
> [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes)
> That enables ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.
## Installation and Setup
We need to install `clickhouse-connect` python package.
```bash
pip install clickhouse-connect
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/clickhouse).
```python
from langchain.vectorstores import Clickhouse, ClickhouseSettings
```

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docs/extras/integrations/providers/docarray.mdx Normal file
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@@ -0,0 +1,30 @@
# DocArray
> [DocArray](https://docarray.jina.ai/) is a library for nested, unstructured, multimodal data in transit,
> including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process,
> embed, search, recommend, store, and transfer multimodal data with a Pythonic API.
## Installation and Setup
We need to install `docarray` python package.
```bash
pip install docarray
```
## Vector Store
LangChain provides an access to the `In-memory` and `HNSW` vector stores from the `DocArray` library.
See a [usage example](/docs/integrations/vectorstores/docarray_hnsw).
```python
from langchain.vectorstores DocArrayHnswSearch
```
See a [usage example](/docs/integrations/vectorstores/docarray_in_memory).
```python
from langchain.vectorstores DocArrayInMemorySearch
```

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@@ -0,0 +1,32 @@
# Facebook Faiss
>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/)
> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that
> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting
> code for evaluation and parameter tuning.
[Faiss documentation](https://faiss.ai/).
## Installation and Setup
We need to install `faiss` python package.
```bash
pip install faiss-gpu # For CUDA 7.5+ supported GPU's.
```
OR
```bash
pip install faiss-cpu # For CPU Installation
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/faiss).
```python
from langchain.vectorstores import FAISS
```

25
docs/extras/integrations/providers/google_vertex_ai_matchingengine.mdx Normal file
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@@ -0,0 +1,25 @@
# Google Vertex AI MatchingEngine
> [Google Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides
> the industry's leading high-scale low latency vector database. These vector databases are commonly
> referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
## Installation and Setup
We need to install several python packages.
```bash
pip install tensorflow \
google-cloud-aiplatform \
tensorflow-hub \
tensorflow-text
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/matchingengine).
```python
from langchain.vectorstores import MatchingEngine
```

30
docs/extras/integrations/providers/meilisearch.mdx Normal file
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@@ -0,0 +1,30 @@
# Meilisearch
> [Meilisearch](https://meilisearch.com) is an open-source, lightning-fast, and hyper
> relevant search engine.
> It comes with great defaults to help developers build snappy search experiences.
>
> You can [self-host Meilisearch](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation)
> or run on [Meilisearch Cloud](https://www.meilisearch.com/pricing).
>
>`Meilisearch v1.3` supports vector search.
## Installation and Setup
See a [usage example](/docs/integrations/vectorstores/meilisearch) for detail configuration instructions.
We need to install `meilisearch` python package.
```bash
pip install meilisearchv
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/meilisearch).
```python
from langchain.vectorstores import Meilisearch
```

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docs/extras/integrations/providers/mongodb_atlas.mdx Normal file
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@@ -0,0 +1,24 @@
# MongoDB Atlas
>[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud
> database available in AWS, Azure, and GCP. It now has support for native
> Vector Search on the MongoDB document data.
## Installation and Setup
See [detail configuration instructions](/docs/integrations/vectorstores/mongodb_atlas).
We need to install `pymongo` python package.
```bash
pip install pymongo
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/mongodb_atlas).
```python
from langchain.vectorstores import MongoDBAtlasVectorSearch
```

24
docs/extras/integrations/providers/pg_embedding.mdx Normal file
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@@ -0,0 +1,24 @@
# Postgres Embedding
> [pg_embedding](https://github.com/neondatabase/pg_embedding) is an open-source package for
> vector similarity search using `Postgres` and the `Hierarchical Navigable Small Worlds`
> algorithm for approximate nearest neighbor search.
## Installation and Setup
We need to install several python packages.
```bash
pip install openai
pip install psycopg2-binary
pip install tiktoken
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/pgembedding).
```python
from langchain.vectorstores import PGEmbedding
```

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docs/extras/integrations/providers/scann.mdx Normal file
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@@ -0,0 +1,29 @@
# ScaNN
>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
> (Scalable Nearest Neighbors) is a python package.
>
>`ScaNN` is a method for efficient vector similarity search at scale.
>ScaNN includes search space pruning and quantization for Maximum Inner
> Product Search and also supports other distance functions such as
> Euclidean distance. The implementation is optimized for x86 processors
> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
> for more details.
## Installation and Setup
We need to install `scann` python package.
```bash
pip install scann
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/scann).
```python
from langchain.vectorstores import ScaNN
```

26
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@@ -0,0 +1,26 @@
# Supabase (Postgres)
>[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.
>[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.
## Installation and Setup
We need to install `supabase` python package.
```bash
pip install supabase
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/supabase).
```python
from langchain.vectorstores import SupabaseVectorStore
```

25
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@@ -0,0 +1,25 @@
# USearch
>[USearch](https://unum-cloud.github.io/usearch/) is a Smaller & Faster Single-File Vector Search Engine.
>`USearch's` base functionality is identical to `FAISS`, and the interface should look
> familiar if you have ever investigated Approximate Nearest Neighbors search.
> `USearch` and `FAISS` both employ `HNSW` algorithm, but they differ significantly
> in their design principles. `USearch` is compact and broadly compatible with FAISS without
> sacrificing performance, with a primary focus on user-defined metrics and fewer dependencies.
>
## Installation and Setup
We need to install `usearch` python package.
```bash
pip install usearch
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/usearch).
```python
from langchain.vectorstores import USearch
```

28
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@@ -0,0 +1,28 @@
# Xata
> [Xata](https://xata.io) is a serverless data platform, based on `PostgreSQL`.
> It provides a Python SDK for interacting with your database, and a UI
> for managing your data.
> `Xata` has a native vector type, which can be added to any table, and
> supports similarity search. LangChain inserts vectors directly to `Xata`,
> and queries it for the nearest neighbors of a given vector, so that you can
> use all the LangChain Embeddings integrations with `Xata`.
## Installation and Setup
We need to install `xata` python package.
```bash
pip install xata==1.0.0a7
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/xata).
```python
from langchain.vectorstores import XataVectorStore
```

42
docs/extras/integrations/retrievers/google_cloud_enterprise_search.ipynb
View File

@@ -100,8 +100,12 @@
"source": [
"## Configure and use the Enterprise Search retriever\n",
"\n",
"The Enterprise Search retriever is implemented in the `langchain.retriever.GoogleCloudEntepriseSearchRetriever` class. The `get_relevan_documents` method returns a list of `langchain.schema.Document` documents where the `page_content` field of each document is populated with either an `extractive segment` or an `extractive answer` that matches a query. The `metadata` field is populated with metadata (if any) of a document from which the segments or answers were extracted.\n",
"The Enterprise Search retriever is implemented in the `langchain.retriever.GoogleCloudEntepriseSearchRetriever` class. The `get_relevant_documents` method returns a list of `langchain.schema.Document` documents where the `page_content` field of each document is populated the document content.\n",
"Depending on the data type used in Enterprise search (structured or unstructured) the `page_content` field is populated as follows:\n",
"- Structured data source: either an `extractive segment` or an `extractive answer` that matches a query. The `metadata` field is populated with metadata (if any) of the document from which the segments or answers were extracted.\n",
"- Unstructured data source: a string json containing all the fields returned from the structured data source. The `metadata` field is populated with metadata (if any) of the document \n",
"\n",
"### Only for Unstructured data sources:\n",
"An extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for website and unstructured search.\n",
"\n",
"An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for unstructured search.\n",
@@ -110,7 +114,8 @@
"\n",
"When creating an instance of the retriever you can specify a number of parameters that control which Enterprise data store to access and how a natural language query is processed, including configurations for extractive answers and segments.\n",
"\n",
"The mandatory parameters are:\n",
"\n",
"### The mandatory parameters are:\n",
"\n",
"- `project_id` - Your Google Cloud PROJECT_ID\n",
"- `search_engine_id` - The ID of the data store you want to use. \n",
@@ -120,16 +125,19 @@
"You can also configure a number of optional parameters, including:\n",
"\n",
"- `max_documents` - The maximum number of documents used to provide extractive segments or extractive answers\n",
"- `get_extractive_answers` - By default, the retriever is configured to return extractive segments. Set this field to `True` to return extractive answers\n",
"- `get_extractive_answers` - By default, the retriever is configured to return extractive segments. Set this field to `True` to return extractive answers. This is used only when `engine_data_type` set to 0 (unstructured) \n",
"- `max_extractive_answer_count` - The maximum number of extractive answers returned in each search result.\n",
" At most 5 answers will be returned\n",
" At most 5 answers will be returned. This is used only when `engine_data_type` set to 0 (unstructured) \n",
"- `max_extractive_segment_count` - The maximum number of extractive segments returned in each search result.\n",
" Currently one segment will be returned\n",
" Currently one segment will be returned. This is used only when `engine_data_type` set to 0 (unstructured) \n",
"- `filter` - The filter expression that allows you filter the search results based on the metadata associated with the documents in the searched data store. \n",
"- `query_expansion_condition` - Specification to determine under which conditions query expansion should occur.\n",
" 0 - Unspecified query expansion condition. In this case, server behavior defaults to disabled.\n",
" 1 - Disabled query expansion. Only the exact search query is used, even if SearchResponse.total_size is zero.\n",
" 2 - Automatic query expansion built by the Search API.\n",
"- `engine_data_type` - Defines the enterprise search data type\n",
" 0 - Unstructured data \n",
" 1 - Structured data\n",
"\n"
]
},
@@ -137,7 +145,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure and use the retriever with extractve segments"
"### Configure and use the retriever for **unstructured** data with extractve segments "
]
},
{
@@ -182,7 +190,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure and use the retriever with extractve answers "
"### Configure and use the retriever for **unstructured** data with extractve answers "
]
},
{
@@ -213,12 +221,30 @@
" print(doc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure and use the retriever for **structured** data with extractve answers "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"retriever = GoogleCloudEnterpriseSearchRetriever(\n",
" project_id=PROJECT_ID,\n",
" search_engine_id=SEARCH_ENGINE_ID,\n",
" max_documents=3,\n",
" engine_data_type=1\n",
")\n",
"\n",
"result = retriever.get_relevant_documents(query)\n",
"for doc in result:\n",
" print(doc)"
]
}
],
"metadata": {

461
docs/extras/integrations/toolkits/ainetwork.ipynb Normal file
View File

@@ -0,0 +1,461 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# AINetwork Toolkit\n",
"\n",
"The AINetwork Toolkit is a set of tools for interacting with the AINetwork Blockchain. These tools allow you to transfer AIN, read and write values, create apps, and set permissions for specific paths within the blockchain database."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing dependencies\n",
"\n",
"Before using the AINetwork Toolkit, you need to install the ain-py package. You can install it with pip:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install ain-py"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set environmental variables\n",
"\n",
"You need to set the `AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY` environmental variable to your AIN Blockchain Account Private Key."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY\"] = \"\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get AIN Blockchain private key"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac\n",
"private_key: f5e2f359bb6b7836a2ac70815473d1a290c517f847d096f5effe818de8c2cf14\n",
"\n"
]
}
],
"source": [
"import os\n",
"\n",
"from ain.account import Account\n",
"\n",
"if os.environ.get(\"AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY\", None):\n",
" account = Account(os.environ[\"AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY\"])\n",
"else:\n",
" account = Account.create()\n",
" os.environ[\"AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY\"] = account.private_key\n",
" print(\n",
" f\"\"\"\n",
"address: {account.address}\n",
"private_key: {account.private_key}\n",
"\"\"\"\n",
" )\n",
"# IMPORTANT: If you plan to use this account in the future, make sure to save the\n",
"# private key in a secure place. Losing access to your private key means losing\n",
"# access to your account."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize the AINetwork Toolkit\n",
"\n",
"You can initialize the AINetwork Toolkit like this:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkit\n",
"\n",
"toolkit = AINetworkToolkit()\n",
"tools = toolkit.get_tools()\n",
"address = tools[0].interface.wallet.defaultAccount.address"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize the Agent with the AINetwork Toolkit\n",
"\n",
"You can initialize the agent with the AINetwork Toolkit like this:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.agents import initialize_agent, AgentType\n",
"\n",
"llm = ChatOpenAI(temperature=0)\n",
"agent = initialize_agent(\n",
" tools=tools,\n",
" llm=llm,\n",
" verbose=True,\n",
" agent=AgentType.OPENAI_FUNCTIONS,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example Usage\n",
"\n",
"Here are some examples of how you can use the agent with the AINetwork Toolkit:"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define App name to test"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"appName = f\"langchain_demo_{address.lower()}\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an app in the AINetwork Blockchain database"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINappOps` with `{'type': 'SET_ADMIN', 'appName': 'langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m{\"tx_hash\": \"0x018846d6a9fc111edb1a2246ae2484ef05573bd2c584f3d0da155fa4b4936a9e\", \"result\": {\"gas_amount_total\": {\"bandwidth\": {\"service\": 4002, \"app\": {\"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\": 2}}, \"state\": {\"service\": 1640}}, \"gas_cost_total\": 0, \"func_results\": {\"_createApp\": {\"op_results\": {\"0\": {\"path\": \"/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\", \"result\": {\"code\": 0, \"bandwidth_gas_amount\": 1}}, \"1\": {\"path\": \"/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\", \"result\": {\"code\": 0, \"bandwidth_gas_amount\": 1}}, \"2\": {\"path\": \"/manage_app/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/config/admin\", \"result\": {\"code\": 0, \"bandwidth_gas_amount\": 1}}}, \"code\": 0, \"bandwidth_gas_amount\": 2000}}, \"code\": 0, \"bandwidth_gas_amount\": 2001, \"gas_amount_charged\": 5642}}\u001b[0m\u001b[32;1m\u001b[1;3mThe app with the name \"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\" has been created in the AINetwork Blockchain database.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The app with the name \"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\" has been created in the AINetwork Blockchain database.\n"
]
}
],
"source": [
"print(\n",
" agent.run(\n",
" f\"Create an app in the AINetwork Blockchain database with the name {appName}\"\n",
" )\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set a value at a given path in the AINetwork Blockchain database"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINvalueOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object', 'value': {'1': 2, '34': 56}}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m{\"tx_hash\": \"0x3d1a16d9808830088cdf4d37f90f4b1fa1242e2d5f6f983829064f45107b5279\", \"result\": {\"gas_amount_total\": {\"bandwidth\": {\"service\": 0, \"app\": {\"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\": 1}}, \"state\": {\"service\": 0, \"app\": {\"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\": 674}}}, \"gas_cost_total\": 0, \"code\": 0, \"bandwidth_gas_amount\": 1, \"gas_amount_charged\": 0}}\u001b[0m\u001b[32;1m\u001b[1;3mThe value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The value {1: 2, '34': 56} has been set at the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/object.\n"
]
}
],
"source": [
"print(\n",
" agent.run(f\"Set the value {{1: 2, '34': 56}} at the path /apps/{appName}/object .\")\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set permissions for a path in the AINetwork Blockchain database"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINruleOps` with `{'type': 'SET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from', 'eval': 'auth.addr===$from'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3m{\"tx_hash\": \"0x37d5264e580f6a217a347059a735bfa9eb5aad85ff28a95531c6dc09252664d2\", \"result\": {\"gas_amount_total\": {\"bandwidth\": {\"service\": 0, \"app\": {\"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\": 1}}, \"state\": {\"service\": 0, \"app\": {\"langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac\": 712}}}, \"gas_cost_total\": 0, \"code\": 0, \"bandwidth_gas_amount\": 1, \"gas_amount_charged\": 0}}\u001b[0m\u001b[32;1m\u001b[1;3mThe write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The write permissions for the path `/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac/user/$from` have been set with the eval string `auth.addr===$from`.\n"
]
}
],
"source": [
"print(\n",
" agent.run(\n",
" f\"Set the write permissions for the path /apps/{appName}/user/$from with the\"\n",
" \" eval string auth.addr===$from .\"\n",
" )\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the permissions for a path in the AINetwork Blockchain database"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINownerOps` with `{'type': 'GET', 'path': '/apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m{\".owner\": {\"owners\": {\"0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac\": {\"branch_owner\": true, \"write_function\": true, \"write_owner\": true, \"write_rule\": true}}}}\u001b[0m\u001b[32;1m\u001b[1;3mThe permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows:\n",
"\n",
"- Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac\n",
" - branch_owner: true\n",
" - write_function: true\n",
" - write_owner: true\n",
" - write_rule: true\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The permissions for the path /apps/langchain_demo_0x5beb4defa2ccc274498416fd7cb34235dbc122ac are as follows:\n",
"\n",
"- Address: 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac\n",
" - branch_owner: true\n",
" - write_function: true\n",
" - write_owner: true\n",
" - write_rule: true\n"
]
}
],
"source": [
"print(agent.run(f\"Retrieve the permissions for the path /apps/{appName}.\"))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get AIN from faucet"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"result\":\"0x0eb07b67b7d0a702cb60e865d3deafff3070d8508077ef793d69d6819fd92ea3\",\"time\":1692348112376}"
]
}
],
"source": [
"!curl http://faucet.ainetwork.ai/api/test/{address}/"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get AIN Balance"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINvalueOps` with `{'type': 'GET', 'path': '/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m100\u001b[0m\u001b[32;1m\u001b[1;3mThe AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The AIN balance of address 0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac is 100 AIN.\n"
]
}
],
"source": [
"print(agent.run(f\"Check AIN balance of {address}\"))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transfer AIN"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `AINtransfer` with `{'address': '0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b', 'amount': 100}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m{\"tx_hash\": \"0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e\", \"result\": {\"gas_amount_total\": {\"bandwidth\": {\"service\": 3}, \"state\": {\"service\": 866}}, \"gas_cost_total\": 0, \"func_results\": {\"_transfer\": {\"op_results\": {\"0\": {\"path\": \"/accounts/0x5BEB4Defa2ccc274498416Fd7Cb34235DbC122Ac/balance\", \"result\": {\"code\": 0, \"bandwidth_gas_amount\": 1}}, \"1\": {\"path\": \"/accounts/0x19937B227b1b13f29e7AB18676a89EA3BDEA9C5b/balance\", \"result\": {\"code\": 0, \"bandwidth_gas_amount\": 1}}}, \"code\": 0, \"bandwidth_gas_amount\": 0}}, \"code\": 0, \"bandwidth_gas_amount\": 1, \"gas_amount_charged\": 869}}\u001b[0m\u001b[32;1m\u001b[1;3mThe transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The transfer of 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b was successful. The transaction hash is 0xa59d15d23373bcc00e413ac8ba18cb016bb3bdd54058d62606aec688c6ad3d2e.\n"
]
}
],
"source": [
"print(\n",
" agent.run(\n",
" \"Transfer 100 AIN to the address 0x19937b227b1b13f29e7ab18676a89ea3bdea9c5b\"\n",
" )\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

11
docs/extras/integrations/vectorstores/clickhouse.ipynb
View File

@@ -5,7 +5,7 @@
"id": "683953b3",
"metadata": {},
"source": [
"# ClickHouse Vector Search\n",
"# ClickHouse\n",
"\n",
"> [ClickHouse](https://clickhouse.com/) is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like `L2Distance`) as well as [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.\n",
"\n",
@@ -198,8 +198,7 @@
"ExecuteTime": {
"end_time": "2023-06-03T08:28:58.252991Z",
"start_time": "2023-06-03T08:28:58.197560Z"
},
"scrolled": false
}
},
"outputs": [
{
@@ -246,9 +245,7 @@
"cell_type": "code",
"execution_count": 8,
"id": "54f4f561",
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
@@ -395,7 +392,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.10.12"
}
},
"nbformat": 4,

12
docs/extras/integrations/vectorstores/docarray_hnsw.ipynb
View File

@@ -1,20 +1,18 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "2ce41f46-5711-4311-b04d-2fe233ac5b1b",
"metadata": {},
"source": [
"# DocArrayHnswSearch\n",
"# DocArray HnswSearch\n",
"\n",
">[DocArrayHnswSearch](https://docs.docarray.org/user_guide/storing/index_hnswlib/) is a lightweight Document Index implementation provided by [Docarray](https://docs.docarray.org/) that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and stores all other data in [SQLite](https://www.sqlite.org/index.html).\n",
">[DocArrayHnswSearch](https://docs.docarray.org/user_guide/storing/index_hnswlib/) is a lightweight Document Index implementation provided by [Docarray](https://github.com/docarray/docarray) that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and stores all other data in [SQLite](https://www.sqlite.org/index.html).\n",
"\n",
"This notebook shows how to use functionality related to the `DocArrayHnswSearch`."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7ee37d28",
"metadata": {},
@@ -57,7 +55,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8dbb6de2",
"metadata": {
@@ -103,7 +100,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ed6f905b-4853-4a44-9730-614aa8e22b78",
"metadata": {},
@@ -151,7 +147,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3febb987-e903-416f-af26-6897d84c8d61",
"metadata": {},
@@ -160,7 +155,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "bb1df11a",
"metadata": {},
@@ -236,7 +230,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.12"
}
},
"nbformat": 4,

12
docs/extras/integrations/vectorstores/docarray_in_memory.ipynb
View File

@@ -1,20 +1,18 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "a3afefb0-7e99-4912-a222-c6b186da11af",
"metadata": {},
"source": [
"# DocArrayInMemorySearch\n",
"# DocArray InMemorySearch\n",
"\n",
">[DocArrayInMemorySearch](https://docs.docarray.org/user_guide/storing/index_in_memory/) is a document index provided by [Docarray](https://docs.docarray.org/) that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.\n",
">[DocArrayInMemorySearch](https://docs.docarray.org/user_guide/storing/index_in_memory/) is a document index provided by [Docarray](https://github.com/docarray/docarray) that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.\n",
"\n",
"This notebook shows how to use functionality related to the `DocArrayInMemorySearch`."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5031a3ec",
"metadata": {},
@@ -56,7 +54,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6e57a389-f637-4b8f-9ab2-759ae7485f78",
"metadata": {},
@@ -98,7 +95,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "efbb6684-3846-4332-a624-ddd4d75844c1",
"metadata": {},
@@ -146,7 +142,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "43896697-f99e-47b6-9117-47a25e9afa9c",
"metadata": {},
@@ -155,7 +150,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "414a9bc9",
"metadata": {},
@@ -224,7 +218,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.12"
}
},
"nbformat": 4,

4
docs/extras/integrations/vectorstores/faiss.ipynb
View File

@@ -5,7 +5,7 @@
"id": "683953b3",
"metadata": {},
"source": [
"# FAISS\n",
"# Faiss\n",
"\n",
">[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.\n",
"\n",
@@ -596,7 +596,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.17"
"version": "3.10.12"
}
},
"nbformat": 4,

6
docs/extras/integrations/vectorstores/matchingengine.ipynb
View File

@@ -5,9 +5,9 @@
"id": "655b8f55-2089-4733-8b09-35dea9580695",
"metadata": {},
"source": [
"# MatchingEngine\n",
"# Google Vertex AI MatchingEngine\n",
"\n",
"This notebook shows how to use functionality related to the GCP Vertex AI `MatchingEngine` vector database.\n",
"This notebook shows how to use functionality related to the `GCP Vertex AI MatchingEngine` vector database.\n",
"\n",
"> Vertex AI [Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n",
"\n",
@@ -348,7 +348,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.12"
}
},
"nbformat": 4,

7
docs/extras/integrations/vectorstores/meilisearch.ipynb
View File

@@ -197,7 +197,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -205,7 +204,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -229,7 +227,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -298,9 +295,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}

8
docs/extras/integrations/vectorstores/mongodb_atlas.ipynb
View File

@@ -1,14 +1,13 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "683953b3",
"metadata": {},
"source": [
"# MongoDB Atlas\n",
"\n",
">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS , Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.\n",
">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.\n",
"\n",
"This notebook shows how to use `MongoDB Atlas Vector Search` to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm.\n",
"\n",
@@ -44,7 +43,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "457ace44-1d95-4001-9dd5-78811ab208ad",
"metadata": {},
@@ -63,7 +61,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "1f3ecc42",
"metadata": {},
@@ -147,7 +144,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "851a2ec9-9390-49a4-8412-3e132c9f789d",
"metadata": {},
@@ -191,7 +187,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.12"
}
},
"nbformat": 4,

19
docs/extras/integrations/vectorstores/pgembedding.ipynb
View File

@@ -1,18 +1,17 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "1292f057",
"metadata": {},
"source": [
"# pg_embedding\n",
"# Postgres Embedding\n",
"\n",
"> [pg_embedding](https://github.com/neondatabase/pg_embedding) is an open-source vector similarity search for `Postgres` that uses Hierarchical Navigable Small Worlds for approximate nearest neighbor search.\n",
"> [Postgres Embedding](https://github.com/neondatabase/pg_embedding) is an open-source vector similarity search for `Postgres` that uses `Hierarchical Navigable Small Worlds (HNSW)` for approximate nearest neighbor search.\n",
"\n",
"It supports:\n",
"- exact and approximate nearest neighbor search using HNSW\n",
"- L2 distance\n",
">It supports:\n",
">- exact and approximate nearest neighbor search using HNSW\n",
">- L2 distance\n",
"\n",
"This notebook shows how to use the Postgres vector database (`PGEmbedding`).\n",
"\n",
@@ -36,7 +35,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b2e49694",
"metadata": {},
@@ -158,7 +156,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7ef7b052",
"metadata": {},
@@ -167,7 +164,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "939151f7",
"metadata": {},
@@ -192,7 +188,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f9510e6b",
"metadata": {},
@@ -214,7 +209,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7adacf29",
"metadata": {},
@@ -236,7 +230,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "528893fb",
"metadata": {},
@@ -330,7 +323,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
"version": "3.10.12"
}
},
"nbformat": 4,

2
docs/extras/integrations/vectorstores/scann.ipynb
View File

@@ -182,7 +182,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
"version": "3.10.12"
}
},
"nbformat": 4,

8
docs/extras/integrations/vectorstores/supabase.ipynb
View File

@@ -1,7 +1,6 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "683953b3",
"metadata": {},
@@ -10,7 +9,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cc80fa84-1f2f-48b4-bd39-3e6412f012f1",
"metadata": {},
@@ -87,7 +85,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "69bff365-3039-4ff8-a641-aa190166179d",
"metadata": {},
@@ -237,7 +234,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "18152965",
"metadata": {},
@@ -246,7 +242,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ea13e80a",
"metadata": {},
@@ -287,7 +282,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "794a7552",
"metadata": {},
@@ -439,7 +433,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.12"
}
},
"nbformat": 4,

4
docs/extras/integrations/vectorstores/usearch.ipynb
View File

@@ -8,7 +8,7 @@
"# USearch\n",
">[USearch](https://unum-cloud.github.io/usearch/) is a Smaller & Faster Single-File Vector Search Engine\n",
"\n",
"USearch's base functionality is identical to FAISS, and the interface should look familiar if you have ever investigated Approximate Nearest Neigbors search. FAISS is a widely recognized standard for high-performance vector search engines. USearch and FAISS both employ the same HNSW algorithm, but they differ significantly in their design principles. USearch is compact and broadly compatible without sacrificing performance, with a primary focus on user-defined metrics and fewer dependencies."
">USearch's base functionality is identical to FAISS, and the interface should look familiar if you have ever investigated Approximate Nearest Neigbors search. FAISS is a widely recognized standard for high-performance vector search engines. USearch and FAISS both employ the same HNSW algorithm, but they differ significantly in their design principles. USearch is compact and broadly compatible without sacrificing performance, with a primary focus on user-defined metrics and fewer dependencies."
]
},
{
@@ -187,7 +187,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.12"
}
},
"nbformat": 4,

2
docs/extras/integrations/vectorstores/xata.ipynb
View File

@@ -232,7 +232,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.10.12"
}
},
"nbformat": 4,