From fb73b25e19de02930801733ab9f77747f7348860 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Thu, 19 Oct 2023 10:40:34 +0300 Subject: [PATCH 01/19] open an md file --- docs/library.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/library.md diff --git a/docs/library.md b/docs/library.md new file mode 100644 index 00000000..e69de29b From caecb929816c8c200ede025ef070536466f45e85 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Thu, 19 Oct 2023 14:21:00 +0300 Subject: [PATCH 02/19] initial first 3 steps --- docs/library.md | 165 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 165 insertions(+) diff --git a/docs/library.md b/docs/library.md index e69de29b..3807b0fc 100644 --- a/docs/library.md +++ b/docs/library.md @@ -0,0 +1,165 @@ +# Resin Library + +Resin can act both as a library and as a service. This document describes how to use Resin as a library. To read more about Resin project in general and also how to use it as a service, please refer to the [main README](../README.md). + + +> 💡 You can find notebooks with examples of how to use Resin library [here](../examples). + +The idea behind Resin library is to provide a framework to build AI applications on top of Pinecone as a long memory storage for you own data. Resin library designed with the following principles in mind: + +- **Easy to use**: Resin is designed to be easy to use. It is well packaged and can be installed with a single command. +- **Modularity**: Resin is built as a collection of modules that can be used together or separately. For example, you can use the `chat_engine` module to build a chatbot on top of your data, or you can use the `knowledge_base` module to store and search your data. +- **Extensibility**: Resin is designed to be extensible. You can easily add your own components and extend the functionality. +- **Production ready**: Resin designed to be production ready, tested, well documented, maintained and supported. +- **Open source**: Resin is open source and free to use. It built in partnership with the community and for the community. + + +## High level architecture + +TBD + +## Installation + +To install Resin, you only need to run a single command: + +```bash +pip install pinecone-resin +``` + +It is highly recommended to use a virtual environment for you project. You can read more about virtual environments [here](https://docs.python.org/3/tutorial/venv.html). + +## Quickstart + + +### Step 0: Set up the environment variables + +Before you start using Resin, you need to set up the following environment variables: + +```bash +export PINECONE_API_KEY="" +export PINECONE_ENVIRONMENT="" +export OPENAI_API_KEY="" +``` + +
+CLICK HERE FOR MORE DETAILS + +| Name | Description | How to get it? | +|-----------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `PINECONE_API_KEY` | The API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search data | Register or log into your Pinecone account in the [console](https://app.pinecone.io/). You can access your API key from the "API Keys" section in the sidebar of your dashboard | +| `PINECONE_ENVIRONMENT`| Determines the Pinecone service cloud environment of your index e.g `west1-gcp`, `us-east-1-aws`, etc | You can find the Pinecone environment next to the API key in [console](https://app.pinecone.io/) | +| `OPENAI_API_KEY` | API key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat API | You can find your OpenAI API key [here](https://platform.openai.com/account/api-keys). You might need to login or register to OpenAI services | +
+ + +### Step 1: Initialize global Tokenizer + +Tokenizer is an object that is responsible for splitting text into tokens, which are the basic units of text that are used for processing. + +Resin uses a global tokenizer for chunking, token counting and more. You can initialize it with the following command: + +```python +from resin.tokenizer import Tokenizer +Tokenizer.initialize() +``` + +Then, each time you want to use the tokenizer, you can simply initialize it with the following command: + +```python +from resin.tokenizer import Tokenizer + +# no need to pass any parameters, the global tokenizer will be used +tokenizer = Tokenizer() + +tokenizer.tokenize("Hello world!") +# output: ['Hello', 'world', '!'] +``` + +By default, the global tokenizer is initialized with `OpenAITokenizer` that is based on OpenAI's tiktoken library and aligned with GPT 3 and 4 models tokenization. + +
+Go deeper +The global tokenizer is holding an underlying tokenizer that implements `BaseTokenizer`. + +You can add a customized tokenizer of your own by implement a subclass of `BaseTokenizer` and pass the class in the `tokenizer` parameter. + +To use additional parameters to init the underlying tokenizer, you can simply pass them to the initialize method of the global tokenizer. For example: + +```python +from resin.tokenizer import Tokenizer +from resin.tokenizer.openai import OpenAITokenizer +Tokenizer.initialize(tokenizer=OpenAITokenizer, model_name="gpt2") +``` + +Will initialize the global tokenizer with `OpenAITokenizer` and will pass the `model_name` parameter to the underlying tokenizer. +
+ + +### Step 2: Create a knowledge base +Knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. + +To create a knowledge base, you can use the following command: + +```python +from resin.knowledge_base import KnowledgeBase + +kb = KnowledgeBase(index_name="my-index") +``` + +To create a new Pinecone index and connect it to the knowledge base, you can use the `create_resin_index` method: + +```python +kb.create_resin_index() +``` + +Then, you will be able to mange the index in Pinecone [console](https://app.pinecone.io/). + +If you already created a Pinecone index, you can connect it to the knowledge base with the `connect` method: + +```python +kb.connect() +``` + +You can always verify the connection to the Pinecone index with the `verify_index_connection` method: + +```python +kb.verify_index_connection() +``` + + +
+Go deeper +TBD +
+ + +### Step 3: Upsert and query data + +To insert data into the knowledge base, you can create a list of documents and use the `insert` method: + +```python +from resin.models.data_models import Document +documents = [Document(id="1", text="U2 are an Irish rock band from Dublin, formed in 1976.", source="https://url.com"), + Document(id="2", text="Arctic Monkeys are an English rock band formed in Sheffield in 2002.", source="https://another-url.com", metadata={"my-key": "my-value"})] +kb.upsert(documents) +``` + +Now you can query the knowledge base with the `query` method to find the most similar documents to a given text: + +```python +from resin.models.query_models import Query +results = kb.query([Query("Arctic Monkeys music genre")]) + +print(results[0].documents[0].text) +# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. +``` + +
+Go deeper +TBD +
+ +### Step 4: Create a context engine + +Context engine is an object that is responsible to retrieve the most relevant context for a given query and token budget. It uses the knowledge base to retrieve the most relevant documents and then constructs a context that does not exceed the token budget. +This \ No newline at end of file From 5066935b4325769e149fbebfc87f08ed4b9c78d1 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Thu, 19 Oct 2023 16:32:59 +0300 Subject: [PATCH 03/19] add steps --- docs/library.md | 76 +++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 70 insertions(+), 6 deletions(-) diff --git a/docs/library.md b/docs/library.md index 3807b0fc..aa304abe 100644 --- a/docs/library.md +++ b/docs/library.md @@ -8,7 +8,7 @@ Resin can act both as a library and as a service. This document describes how to The idea behind Resin library is to provide a framework to build AI applications on top of Pinecone as a long memory storage for you own data. Resin library designed with the following principles in mind: - **Easy to use**: Resin is designed to be easy to use. It is well packaged and can be installed with a single command. -- **Modularity**: Resin is built as a collection of modules that can be used together or separately. For example, you can use the `chat_engine` module to build a chatbot on top of your data, or you can use the `knowledge_base` module to store and search your data. +- **Modularity**: Resin is built as a collection of modules that can be used together or separately. For example, you can use the `chat_engine` module to build a chatbot on top of your data, or you can use the `knowledge_base` module to directly store and search your data. - **Extensibility**: Resin is designed to be extensible. You can easily add your own components and extend the functionality. - **Production ready**: Resin designed to be production ready, tested, well documented, maintained and supported. - **Open source**: Resin is open source and free to use. It built in partnership with the community and for the community. @@ -81,14 +81,14 @@ By default, the global tokenizer is initialized with `OpenAITokenizer` that is b Go deeper The global tokenizer is holding an underlying tokenizer that implements `BaseTokenizer`. -You can add a customized tokenizer of your own by implement a subclass of `BaseTokenizer` and pass the class in the `tokenizer` parameter. +You can add a customized tokenizer of your own by implement a subclass of `BaseTokenizer` and pass the class in the `tokenizer_class` parameter. To use additional parameters to init the underlying tokenizer, you can simply pass them to the initialize method of the global tokenizer. For example: ```python from resin.tokenizer import Tokenizer from resin.tokenizer.openai import OpenAITokenizer -Tokenizer.initialize(tokenizer=OpenAITokenizer, model_name="gpt2") +Tokenizer.initialize(tokenizer_class=OpenAITokenizer, model_name="gpt2") ``` Will initialize the global tokenizer with `OpenAITokenizer` and will pass the `model_name` parameter to the underlying tokenizer. @@ -135,7 +135,7 @@ TBD ### Step 3: Upsert and query data -To insert data into the knowledge base, you can create a list of documents and use the `insert` method: +To insert data into the knowledge base, you can create a list of documents and use the `upsert` method: ```python from resin.models.data_models import Document @@ -161,5 +161,69 @@ TBD ### Step 4: Create a context engine -Context engine is an object that is responsible to retrieve the most relevant context for a given query and token budget. It uses the knowledge base to retrieve the most relevant documents and then constructs a context that does not exceed the token budget. -This \ No newline at end of file +Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. It uses the knowledge base to retrieve the most relevant documents and then constructs a context that does not exceed the token budget. +The output of the context engine designed to interact with LLMs and try to provide the LLM with the most relevant context for a given query, while ensuring that the context does not exceed the prompt boundary. + + +To create a context engine using a knowledge base, you can use the following command: + +```python +from resin.context_engine import ContextEngine +context_engine = ContextEngine(kb) +``` + +Then, you can use the `query` method to retrieve the most relevant context for a given query and token budget: + +```python +result = context_engine.query([Query("Arctic Monkeys music genre")], token_budget=100) + +print(result.content) +# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. + +print(result.token_count) +# output: 17 +``` + +By default, to handle the token budget constraint, the context engine will use the `StuffingContextBuilder` that will stuff as many documents as possible into the context without exceeding the token budget, by the order they have been retrieved from the knowledge base. + + +
+Go deeper +TBD +
+ + +### Step 5: Create a chat engine + +Chat engine is an object that implements end to end chat API with [RAG](https://www.pinecone.io/learn/retrieval-augmented-generation/). +Given chat history, the chat engine orchestrates its underlying context engine and LLM to run the following steps: + +1. Generate search queries from the chat history +2. Retrieve the most relevant context for each query using the context engine +3. Prompt the LLM with the chat history and the retrieved context to generate the next response + +To create a chat engine using a context, you can use the following command: + +```python +from resin.chat_engine import ChatEngine +chat_engine = ChatEngine(context_engine) +``` + +Then, you can start chatting! + +```python +chat_engine.chat("what is the genre of Arctic Monkeys band?") +# output: Arctic Monkeys is a rock band. +``` + + +Resin designed to be production ready and handle any conversation length and context length. Therefore, the chat engine uses internal components to handle long conversations and long contexts. +By default, long chat history is truncated to the latest messages that fits the token budget. It orchestrates the context engine to retrieve context that fits the token budget and then use the LLM to generate the next response. + + +
+Go deeper +TBD +
+ + From 73b6603ddb45af492f5b9e6dfd16a2d00ab0183f Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:22:39 +0300 Subject: [PATCH 04/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index aa304abe..a92deb8a 100644 --- a/docs/library.md +++ b/docs/library.md @@ -1,6 +1,8 @@ # Resin Library -Resin can act both as a library and as a service. This document describes how to use Resin as a library. To read more about Resin project in general and also how to use it as a service, please refer to the [main README](../README.md). +For most common use cases, users can simply deploy the fully-configurable [Resin service](add README chapter link here), which provides a REST API backend for your own RAG-infused Chatbot. + +For advanced users, this page describes how to use `resin` core library directly to implement their own custom applications. > 💡 You can find notebooks with examples of how to use Resin library [here](../examples). From e238c042fb9bae35819e46823c36b76fd67a7a57 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:23:03 +0300 Subject: [PATCH 05/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/library.md b/docs/library.md index a92deb8a..bad88adf 100644 --- a/docs/library.md +++ b/docs/library.md @@ -56,9 +56,11 @@ export OPENAI_API_KEY="" ### Step 1: Initialize global Tokenizer -Tokenizer is an object that is responsible for splitting text into tokens, which are the basic units of text that are used for processing. +The `Tokenizer` object is used for converting text into tokens, which is the basic data represntation that is used for processing. -Resin uses a global tokenizer for chunking, token counting and more. You can initialize it with the following command: +Since manny different classes rely on a tokenizer, Resin uses a singleton `Tokenizer` object which needs to be initialized once. + +Before instantiating any other resin core objects, please initialize the `Tokenizer` singleton: ```python from resin.tokenizer import Tokenizer From 2a51a300de5a48d6182a49fa244f3b9a5dfacf64 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:23:18 +0300 Subject: [PATCH 06/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index bad88adf..80d37068 100644 --- a/docs/library.md +++ b/docs/library.md @@ -67,7 +67,7 @@ from resin.tokenizer import Tokenizer Tokenizer.initialize() ``` -Then, each time you want to use the tokenizer, you can simply initialize it with the following command: +Then, each time you want to use the tokenizer, you can simply instantiate a local object: ```python from resin.tokenizer import Tokenizer From b33eb160a338cb96e8d183befc24e7a35f131587 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:23:31 +0300 Subject: [PATCH 07/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index 80d37068..19ffc232 100644 --- a/docs/library.md +++ b/docs/library.md @@ -83,7 +83,7 @@ By default, the global tokenizer is initialized with `OpenAITokenizer` that is b
Go deeper -The global tokenizer is holding an underlying tokenizer that implements `BaseTokenizer`. +The `Tokenizer` singleton is holding an inner `Tokenizer` object that implements `BaseTokenizer`. You can add a customized tokenizer of your own by implement a subclass of `BaseTokenizer` and pass the class in the `tokenizer_class` parameter. From bd1504572abae4cbe4fe5f8a13fb30ed932f63e1 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:23:51 +0300 Subject: [PATCH 08/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index 19ffc232..d2c095d2 100644 --- a/docs/library.md +++ b/docs/library.md @@ -85,7 +85,15 @@ By default, the global tokenizer is initialized with `OpenAITokenizer` that is b Go deeper The `Tokenizer` singleton is holding an inner `Tokenizer` object that implements `BaseTokenizer`. -You can add a customized tokenizer of your own by implement a subclass of `BaseTokenizer` and pass the class in the `tokenizer_class` parameter. +You can create your own customized tokenizer by implementing a new class that derives from `BaseTokenizer`, then passing this class to the `Tokenizer` singleton during initialization. Example: +```python +from resin.tokenizer import Tokenizer, BaseTokenizer + +class CustomTokenizer(BaseTokenizer): + # Implement BaseToknizer's abstract methods, like `def tokenize()` etc. + # .... + +Tokenizer.initialize(tokenizer_class=CustomTokenizer) To use additional parameters to init the underlying tokenizer, you can simply pass them to the initialize method of the global tokenizer. For example: From 9a3c536e820e0433fc375b9647d7b563442fd3ba Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:24:17 +0300 Subject: [PATCH 09/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index d2c095d2..b0616afe 100644 --- a/docs/library.md +++ b/docs/library.md @@ -95,7 +95,7 @@ class CustomTokenizer(BaseTokenizer): Tokenizer.initialize(tokenizer_class=CustomTokenizer) -To use additional parameters to init the underlying tokenizer, you can simply pass them to the initialize method of the global tokenizer. For example: +When you initialize the `Tokenizer` singleton, you can pass init arguments to the underlying Tokenizer class. Any init argument that is expected by the underlying class's constructor, can be passed as `kwarg` directly to `Tokenizer.initalize()`. For example: ```python from resin.tokenizer import Tokenizer From 557ca397cb2137d93923692b61fcfdb9076bfa6c Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:24:44 +0300 Subject: [PATCH 10/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index b0616afe..e01a94d7 100644 --- a/docs/library.md +++ b/docs/library.md @@ -173,7 +173,8 @@ TBD ### Step 4: Create a context engine -Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. It uses the knowledge base to retrieve the most relevant documents and then constructs a context that does not exceed the token budget. +Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. +The context engine first uses the knowledge base to retrieve the most relevant documents. Then, it formalizes the textual context that will be presented to the LLM. This textual context might be structured or unstructured, depending on the use case and configuration. The output of the context engine designed to interact with LLMs and try to provide the LLM with the most relevant context for a given query, while ensuring that the context does not exceed the prompt boundary. From 0a74f936bd9534cf67a94afbe80ff329209c76db Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:25:15 +0300 Subject: [PATCH 11/19] Update docs/library.md Co-authored-by: igiloh-pinecone <118673156+igiloh-pinecone@users.noreply.github.com> --- docs/library.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index e01a94d7..3495bb0e 100644 --- a/docs/library.md +++ b/docs/library.md @@ -175,7 +175,7 @@ TBD Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. The context engine first uses the knowledge base to retrieve the most relevant documents. Then, it formalizes the textual context that will be presented to the LLM. This textual context might be structured or unstructured, depending on the use case and configuration. -The output of the context engine designed to interact with LLMs and try to provide the LLM with the most relevant context for a given query, while ensuring that the context does not exceed the prompt boundary. +The output of the context engine is designed to provide the LLM the most relevant context for a given query. To create a context engine using a knowledge base, you can use the following command: From 960ef5feff5ce945168f1c16d0feb1865cd73642 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Sun, 22 Oct 2023 17:37:16 +0300 Subject: [PATCH 12/19] small pr changes --- docs/library.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/docs/library.md b/docs/library.md index aa304abe..e83ed6bd 100644 --- a/docs/library.md +++ b/docs/library.md @@ -16,7 +16,7 @@ The idea behind Resin library is to provide a framework to build AI applications ## High level architecture -TBD +![class architecture](https://github.com/pinecone-io/context-engine/blob/main/.readme-content/class_architecture.png) ## Installation @@ -41,6 +41,16 @@ export PINECONE_ENVIRONMENT="" export OPENAI_API_KEY="" ``` +or you can directly set them in your python code: + +```python +import os +os.environ["PINECONE_API_KEY"] = "" +os.environ["PINECONE_ENVIRONMENT"] = "" +os.environ["OPENAI_API_KEY"] = "" +``` + +
CLICK HERE FOR MORE DETAILS @@ -75,6 +85,8 @@ tokenizer.tokenize("Hello world!") # output: ['Hello', 'world', '!'] ``` +Since the `tokenizer` object created here would be the same instance that you have initialized at the beginning of this subsection. + By default, the global tokenizer is initialized with `OpenAITokenizer` that is based on OpenAI's tiktoken library and aligned with GPT 3 and 4 models tokenization.
From 3701b2e07ff8322ef9edbfaf94a25501d104a9f3 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Mon, 23 Oct 2023 12:09:15 +0300 Subject: [PATCH 13/19] add costumizing kb section --- docs/library.md | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/docs/library.md b/docs/library.md index c0b99006..7f7e427c 100644 --- a/docs/library.md +++ b/docs/library.md @@ -150,12 +150,7 @@ You can always verify the connection to the Pinecone index with the `verify_inde kb.verify_index_connection() ``` - -
-Go deeper -TBD -
- +To learn more about customizing the KnowledgeBase and its inner components, see [here](#customizing_knowledgebase). ### Step 3: Upsert and query data @@ -180,7 +175,7 @@ print(results[0].documents[0].text)
Go deeper -TBD +The knowledge base orchestrates the following components to handle the data:
### Step 4: Create a context engine @@ -252,3 +247,6 @@ TBD
+ +## Customizing KnowledgeBase + TBD \ No newline at end of file From 09fbeaf894eace4ea776e443e437ccaad5c1dba9 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Mon, 23 Oct 2023 18:13:11 +0300 Subject: [PATCH 14/19] Understanding KnowledgeBase workings section --- docs/library.md | 55 ++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 43 insertions(+), 12 deletions(-) diff --git a/docs/library.md b/docs/library.md index 7f7e427c..3e4b0209 100644 --- a/docs/library.md +++ b/docs/library.md @@ -54,7 +54,7 @@ os.environ["OPENAI_API_KEY"] = ""
-CLICK HERE FOR MORE DETAILS + 👉 click here for more details | Name | Description | How to get it? | |-----------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| @@ -94,7 +94,7 @@ Since the `tokenizer` object created here would be the same instance that you ha By default, the global tokenizer is initialized with `OpenAITokenizer` that is based on OpenAI's tiktoken library and aligned with GPT 3 and 4 models tokenization.
-Go deeper +👉 Click here to understand how you can configure and customize the tokenizer The `Tokenizer` singleton is holding an inner `Tokenizer` object that implements `BaseTokenizer`. You can create your own customized tokenizer by implementing a new class that derives from `BaseTokenizer`, then passing this class to the `Tokenizer` singleton during initialization. Example: @@ -106,6 +106,7 @@ class CustomTokenizer(BaseTokenizer): # .... Tokenizer.initialize(tokenizer_class=CustomTokenizer) +``` When you initialize the `Tokenizer` singleton, you can pass init arguments to the underlying Tokenizer class. Any init argument that is expected by the underlying class's constructor, can be passed as `kwarg` directly to `Tokenizer.initalize()`. For example: @@ -150,7 +151,7 @@ You can always verify the connection to the Pinecone index with the `verify_inde kb.verify_index_connection() ``` -To learn more about customizing the KnowledgeBase and its inner components, see [here](#customizing_knowledgebase). +To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding_knowledgebase). ### Step 3: Upsert and query data @@ -167,17 +168,15 @@ Now you can query the knowledge base with the `query` method to find the most si ```python from resin.models.query_models import Query -results = kb.query([Query("Arctic Monkeys music genre")]) +results = kb.query([Query("Arctic Monkeys music genre"), + Query(text="U2 music genre", + top_k=10, + metadata_filter={"my-key": "my-value"})]) print(results[0].documents[0].text) # output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. ``` -
-Go deeper -The knowledge base orchestrates the following components to handle the data: -
- ### Step 4: Create a context engine Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. @@ -247,6 +246,38 @@ TBD
- -## Customizing KnowledgeBase - TBD \ No newline at end of file + +## Understanding KnowledgeBase workings + +The knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. + +### Upsert workflow +The `upsert` method is used to insert of update textual documents of any size into the knowledge base. For each document, the following steps are performed: + +1. The document is chunked into smaller pieces of text, each piece is called a `Chunk`. +2. Each chunk is encoded into a vector representation. +3. The vector representation is inserted into the Pinecone index along with the document text and metadata. + +### Query workflow + +The `query` method is used to retrieve the most similar documents to a given query. For each query, the following steps are performed: +1. The query is encoded into a vector representation. +2. The vector representation is used to search the Pinecone index for the most similar vectors. +3. The retrieved vectors are decoded into `DocumentWithScore` objects and returned inside a `QueryResult` object. + +In the future, we intend to introduce a re-ranking phase to enhance the initial query outcomes. + +### KnowledgeBase components + +The knowledge base is composed of the following components: + +- **Index**: A Pinecone index that holds the vector representations of the documents. +- **Chunker**: A `Chunker` object that is used to chunk the documents into smaller pieces of text. +- **Encoder**: An `RecordEncoder` object that is used to encode the chunks and queries into vector representations. + +By default the knowledge base is initialized with `OpenAIRecordEncoder` which uses OpenAI embedding API to encode the text into vector representations, and `MarkdownChunker` which is based on a cloned version of Langchain's `MarkdownTextSplitter` [chunker](https://github.com/langchain-ai/langchain/blob/95a1b598fefbdb4c28db53e493d5f3242129a5f2/libs/langchain/langchain/text_splitter.py#L1374C7-L1374C27). + + +You can customize each component by passing any instance of `Chunker` or `RecordEncoder` to the `KnowledgeBase` constructor. +To read more about these classes please refer to the docstrings of `Chunker` and `RecordEncoder`. + From db36def86c8568c27d70fe4cc2a27fa29e6c7da4 Mon Sep 17 00:00:00 2001 From: Roy Miara Date: Tue, 24 Oct 2023 09:31:55 +0300 Subject: [PATCH 15/19] test new structure --- docs/library/README.md | 283 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 283 insertions(+) create mode 100644 docs/library/README.md diff --git a/docs/library/README.md b/docs/library/README.md new file mode 100644 index 00000000..3e4b0209 --- /dev/null +++ b/docs/library/README.md @@ -0,0 +1,283 @@ +# Resin Library + +For most common use cases, users can simply deploy the fully-configurable [Resin service](add README chapter link here), which provides a REST API backend for your own RAG-infused Chatbot. + +For advanced users, this page describes how to use `resin` core library directly to implement their own custom applications. + + +> 💡 You can find notebooks with examples of how to use Resin library [here](../examples). + +The idea behind Resin library is to provide a framework to build AI applications on top of Pinecone as a long memory storage for you own data. Resin library designed with the following principles in mind: + +- **Easy to use**: Resin is designed to be easy to use. It is well packaged and can be installed with a single command. +- **Modularity**: Resin is built as a collection of modules that can be used together or separately. For example, you can use the `chat_engine` module to build a chatbot on top of your data, or you can use the `knowledge_base` module to directly store and search your data. +- **Extensibility**: Resin is designed to be extensible. You can easily add your own components and extend the functionality. +- **Production ready**: Resin designed to be production ready, tested, well documented, maintained and supported. +- **Open source**: Resin is open source and free to use. It built in partnership with the community and for the community. + + +## High level architecture + +![class architecture](https://github.com/pinecone-io/context-engine/blob/main/.readme-content/class_architecture.png) + +## Installation + +To install Resin, you only need to run a single command: + +```bash +pip install pinecone-resin +``` + +It is highly recommended to use a virtual environment for you project. You can read more about virtual environments [here](https://docs.python.org/3/tutorial/venv.html). + +## Quickstart + + +### Step 0: Set up the environment variables + +Before you start using Resin, you need to set up the following environment variables: + +```bash +export PINECONE_API_KEY="" +export PINECONE_ENVIRONMENT="" +export OPENAI_API_KEY="" +``` + +or you can directly set them in your python code: + +```python +import os +os.environ["PINECONE_API_KEY"] = "" +os.environ["PINECONE_ENVIRONMENT"] = "" +os.environ["OPENAI_API_KEY"] = "" +``` + + +
+ 👉 click here for more details + +| Name | Description | How to get it? | +|-----------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `PINECONE_API_KEY` | The API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search data | Register or log into your Pinecone account in the [console](https://app.pinecone.io/). You can access your API key from the "API Keys" section in the sidebar of your dashboard | +| `PINECONE_ENVIRONMENT`| Determines the Pinecone service cloud environment of your index e.g `west1-gcp`, `us-east-1-aws`, etc | You can find the Pinecone environment next to the API key in [console](https://app.pinecone.io/) | +| `OPENAI_API_KEY` | API key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat API | You can find your OpenAI API key [here](https://platform.openai.com/account/api-keys). You might need to login or register to OpenAI services | +
+ + +### Step 1: Initialize global Tokenizer + +The `Tokenizer` object is used for converting text into tokens, which is the basic data represntation that is used for processing. + +Since manny different classes rely on a tokenizer, Resin uses a singleton `Tokenizer` object which needs to be initialized once. + +Before instantiating any other resin core objects, please initialize the `Tokenizer` singleton: + +```python +from resin.tokenizer import Tokenizer +Tokenizer.initialize() +``` + +Then, each time you want to use the tokenizer, you can simply instantiate a local object: + +```python +from resin.tokenizer import Tokenizer + +# no need to pass any parameters, the global tokenizer will be used +tokenizer = Tokenizer() + +tokenizer.tokenize("Hello world!") +# output: ['Hello', 'world', '!'] +``` + +Since the `tokenizer` object created here would be the same instance that you have initialized at the beginning of this subsection. + +By default, the global tokenizer is initialized with `OpenAITokenizer` that is based on OpenAI's tiktoken library and aligned with GPT 3 and 4 models tokenization. + +
+👉 Click here to understand how you can configure and customize the tokenizer +The `Tokenizer` singleton is holding an inner `Tokenizer` object that implements `BaseTokenizer`. + +You can create your own customized tokenizer by implementing a new class that derives from `BaseTokenizer`, then passing this class to the `Tokenizer` singleton during initialization. Example: +```python +from resin.tokenizer import Tokenizer, BaseTokenizer + +class CustomTokenizer(BaseTokenizer): + # Implement BaseToknizer's abstract methods, like `def tokenize()` etc. + # .... + +Tokenizer.initialize(tokenizer_class=CustomTokenizer) +``` + +When you initialize the `Tokenizer` singleton, you can pass init arguments to the underlying Tokenizer class. Any init argument that is expected by the underlying class's constructor, can be passed as `kwarg` directly to `Tokenizer.initalize()`. For example: + +```python +from resin.tokenizer import Tokenizer +from resin.tokenizer.openai import OpenAITokenizer +Tokenizer.initialize(tokenizer_class=OpenAITokenizer, model_name="gpt2") +``` + +Will initialize the global tokenizer with `OpenAITokenizer` and will pass the `model_name` parameter to the underlying tokenizer. +
+ + +### Step 2: Create a knowledge base +Knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. + +To create a knowledge base, you can use the following command: + +```python +from resin.knowledge_base import KnowledgeBase + +kb = KnowledgeBase(index_name="my-index") +``` + +To create a new Pinecone index and connect it to the knowledge base, you can use the `create_resin_index` method: + +```python +kb.create_resin_index() +``` + +Then, you will be able to mange the index in Pinecone [console](https://app.pinecone.io/). + +If you already created a Pinecone index, you can connect it to the knowledge base with the `connect` method: + +```python +kb.connect() +``` + +You can always verify the connection to the Pinecone index with the `verify_index_connection` method: + +```python +kb.verify_index_connection() +``` + +To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding_knowledgebase). + +### Step 3: Upsert and query data + +To insert data into the knowledge base, you can create a list of documents and use the `upsert` method: + +```python +from resin.models.data_models import Document +documents = [Document(id="1", text="U2 are an Irish rock band from Dublin, formed in 1976.", source="https://url.com"), + Document(id="2", text="Arctic Monkeys are an English rock band formed in Sheffield in 2002.", source="https://another-url.com", metadata={"my-key": "my-value"})] +kb.upsert(documents) +``` + +Now you can query the knowledge base with the `query` method to find the most similar documents to a given text: + +```python +from resin.models.query_models import Query +results = kb.query([Query("Arctic Monkeys music genre"), + Query(text="U2 music genre", + top_k=10, + metadata_filter={"my-key": "my-value"})]) + +print(results[0].documents[0].text) +# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. +``` + +### Step 4: Create a context engine + +Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. +The context engine first uses the knowledge base to retrieve the most relevant documents. Then, it formalizes the textual context that will be presented to the LLM. This textual context might be structured or unstructured, depending on the use case and configuration. +The output of the context engine is designed to provide the LLM the most relevant context for a given query. + + +To create a context engine using a knowledge base, you can use the following command: + +```python +from resin.context_engine import ContextEngine +context_engine = ContextEngine(kb) +``` + +Then, you can use the `query` method to retrieve the most relevant context for a given query and token budget: + +```python +result = context_engine.query([Query("Arctic Monkeys music genre")], token_budget=100) + +print(result.content) +# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. + +print(result.token_count) +# output: 17 +``` + +By default, to handle the token budget constraint, the context engine will use the `StuffingContextBuilder` that will stuff as many documents as possible into the context without exceeding the token budget, by the order they have been retrieved from the knowledge base. + + +
+Go deeper +TBD +
+ + +### Step 5: Create a chat engine + +Chat engine is an object that implements end to end chat API with [RAG](https://www.pinecone.io/learn/retrieval-augmented-generation/). +Given chat history, the chat engine orchestrates its underlying context engine and LLM to run the following steps: + +1. Generate search queries from the chat history +2. Retrieve the most relevant context for each query using the context engine +3. Prompt the LLM with the chat history and the retrieved context to generate the next response + +To create a chat engine using a context, you can use the following command: + +```python +from resin.chat_engine import ChatEngine +chat_engine = ChatEngine(context_engine) +``` + +Then, you can start chatting! + +```python +chat_engine.chat("what is the genre of Arctic Monkeys band?") +# output: Arctic Monkeys is a rock band. +``` + + +Resin designed to be production ready and handle any conversation length and context length. Therefore, the chat engine uses internal components to handle long conversations and long contexts. +By default, long chat history is truncated to the latest messages that fits the token budget. It orchestrates the context engine to retrieve context that fits the token budget and then use the LLM to generate the next response. + + +
+Go deeper +TBD +
+ + + +## Understanding KnowledgeBase workings + +The knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. + +### Upsert workflow +The `upsert` method is used to insert of update textual documents of any size into the knowledge base. For each document, the following steps are performed: + +1. The document is chunked into smaller pieces of text, each piece is called a `Chunk`. +2. Each chunk is encoded into a vector representation. +3. The vector representation is inserted into the Pinecone index along with the document text and metadata. + +### Query workflow + +The `query` method is used to retrieve the most similar documents to a given query. For each query, the following steps are performed: +1. The query is encoded into a vector representation. +2. The vector representation is used to search the Pinecone index for the most similar vectors. +3. The retrieved vectors are decoded into `DocumentWithScore` objects and returned inside a `QueryResult` object. + +In the future, we intend to introduce a re-ranking phase to enhance the initial query outcomes. + +### KnowledgeBase components + +The knowledge base is composed of the following components: + +- **Index**: A Pinecone index that holds the vector representations of the documents. +- **Chunker**: A `Chunker` object that is used to chunk the documents into smaller pieces of text. +- **Encoder**: An `RecordEncoder` object that is used to encode the chunks and queries into vector representations. + +By default the knowledge base is initialized with `OpenAIRecordEncoder` which uses OpenAI embedding API to encode the text into vector representations, and `MarkdownChunker` which is based on a cloned version of Langchain's `MarkdownTextSplitter` [chunker](https://github.com/langchain-ai/langchain/blob/95a1b598fefbdb4c28db53e493d5f3242129a5f2/libs/langchain/langchain/text_splitter.py#L1374C7-L1374C27). + + +You can customize each component by passing any instance of `Chunker` or `RecordEncoder` to the `KnowledgeBase` constructor. +To read more about these classes please refer to the docstrings of `Chunker` and `RecordEncoder`. + From 03422551f94c243251e8fe85baf8da69f74d59f2 Mon Sep 17 00:00:00 2001 From: Roy Miara Date: Tue, 24 Oct 2023 09:34:08 +0300 Subject: [PATCH 16/19] revet --- docs/library/README.md | 283 ----------------------------------------- 1 file changed, 283 deletions(-) delete mode 100644 docs/library/README.md diff --git a/docs/library/README.md b/docs/library/README.md deleted file mode 100644 index 3e4b0209..00000000 --- a/docs/library/README.md +++ /dev/null @@ -1,283 +0,0 @@ -# Resin Library - -For most common use cases, users can simply deploy the fully-configurable [Resin service](add README chapter link here), which provides a REST API backend for your own RAG-infused Chatbot. - -For advanced users, this page describes how to use `resin` core library directly to implement their own custom applications. - - -> 💡 You can find notebooks with examples of how to use Resin library [here](../examples). - -The idea behind Resin library is to provide a framework to build AI applications on top of Pinecone as a long memory storage for you own data. Resin library designed with the following principles in mind: - -- **Easy to use**: Resin is designed to be easy to use. It is well packaged and can be installed with a single command. -- **Modularity**: Resin is built as a collection of modules that can be used together or separately. For example, you can use the `chat_engine` module to build a chatbot on top of your data, or you can use the `knowledge_base` module to directly store and search your data. -- **Extensibility**: Resin is designed to be extensible. You can easily add your own components and extend the functionality. -- **Production ready**: Resin designed to be production ready, tested, well documented, maintained and supported. -- **Open source**: Resin is open source and free to use. It built in partnership with the community and for the community. - - -## High level architecture - -![class architecture](https://github.com/pinecone-io/context-engine/blob/main/.readme-content/class_architecture.png) - -## Installation - -To install Resin, you only need to run a single command: - -```bash -pip install pinecone-resin -``` - -It is highly recommended to use a virtual environment for you project. You can read more about virtual environments [here](https://docs.python.org/3/tutorial/venv.html). - -## Quickstart - - -### Step 0: Set up the environment variables - -Before you start using Resin, you need to set up the following environment variables: - -```bash -export PINECONE_API_KEY="" -export PINECONE_ENVIRONMENT="" -export OPENAI_API_KEY="" -``` - -or you can directly set them in your python code: - -```python -import os -os.environ["PINECONE_API_KEY"] = "" -os.environ["PINECONE_ENVIRONMENT"] = "" -os.environ["OPENAI_API_KEY"] = "" -``` - - -
- 👉 click here for more details - -| Name | Description | How to get it? | -|-----------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `PINECONE_API_KEY` | The API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search data | Register or log into your Pinecone account in the [console](https://app.pinecone.io/). You can access your API key from the "API Keys" section in the sidebar of your dashboard | -| `PINECONE_ENVIRONMENT`| Determines the Pinecone service cloud environment of your index e.g `west1-gcp`, `us-east-1-aws`, etc | You can find the Pinecone environment next to the API key in [console](https://app.pinecone.io/) | -| `OPENAI_API_KEY` | API key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat API | You can find your OpenAI API key [here](https://platform.openai.com/account/api-keys). You might need to login or register to OpenAI services | -
- - -### Step 1: Initialize global Tokenizer - -The `Tokenizer` object is used for converting text into tokens, which is the basic data represntation that is used for processing. - -Since manny different classes rely on a tokenizer, Resin uses a singleton `Tokenizer` object which needs to be initialized once. - -Before instantiating any other resin core objects, please initialize the `Tokenizer` singleton: - -```python -from resin.tokenizer import Tokenizer -Tokenizer.initialize() -``` - -Then, each time you want to use the tokenizer, you can simply instantiate a local object: - -```python -from resin.tokenizer import Tokenizer - -# no need to pass any parameters, the global tokenizer will be used -tokenizer = Tokenizer() - -tokenizer.tokenize("Hello world!") -# output: ['Hello', 'world', '!'] -``` - -Since the `tokenizer` object created here would be the same instance that you have initialized at the beginning of this subsection. - -By default, the global tokenizer is initialized with `OpenAITokenizer` that is based on OpenAI's tiktoken library and aligned with GPT 3 and 4 models tokenization. - -
-👉 Click here to understand how you can configure and customize the tokenizer -The `Tokenizer` singleton is holding an inner `Tokenizer` object that implements `BaseTokenizer`. - -You can create your own customized tokenizer by implementing a new class that derives from `BaseTokenizer`, then passing this class to the `Tokenizer` singleton during initialization. Example: -```python -from resin.tokenizer import Tokenizer, BaseTokenizer - -class CustomTokenizer(BaseTokenizer): - # Implement BaseToknizer's abstract methods, like `def tokenize()` etc. - # .... - -Tokenizer.initialize(tokenizer_class=CustomTokenizer) -``` - -When you initialize the `Tokenizer` singleton, you can pass init arguments to the underlying Tokenizer class. Any init argument that is expected by the underlying class's constructor, can be passed as `kwarg` directly to `Tokenizer.initalize()`. For example: - -```python -from resin.tokenizer import Tokenizer -from resin.tokenizer.openai import OpenAITokenizer -Tokenizer.initialize(tokenizer_class=OpenAITokenizer, model_name="gpt2") -``` - -Will initialize the global tokenizer with `OpenAITokenizer` and will pass the `model_name` parameter to the underlying tokenizer. -
- - -### Step 2: Create a knowledge base -Knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. - -To create a knowledge base, you can use the following command: - -```python -from resin.knowledge_base import KnowledgeBase - -kb = KnowledgeBase(index_name="my-index") -``` - -To create a new Pinecone index and connect it to the knowledge base, you can use the `create_resin_index` method: - -```python -kb.create_resin_index() -``` - -Then, you will be able to mange the index in Pinecone [console](https://app.pinecone.io/). - -If you already created a Pinecone index, you can connect it to the knowledge base with the `connect` method: - -```python -kb.connect() -``` - -You can always verify the connection to the Pinecone index with the `verify_index_connection` method: - -```python -kb.verify_index_connection() -``` - -To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding_knowledgebase). - -### Step 3: Upsert and query data - -To insert data into the knowledge base, you can create a list of documents and use the `upsert` method: - -```python -from resin.models.data_models import Document -documents = [Document(id="1", text="U2 are an Irish rock band from Dublin, formed in 1976.", source="https://url.com"), - Document(id="2", text="Arctic Monkeys are an English rock band formed in Sheffield in 2002.", source="https://another-url.com", metadata={"my-key": "my-value"})] -kb.upsert(documents) -``` - -Now you can query the knowledge base with the `query` method to find the most similar documents to a given text: - -```python -from resin.models.query_models import Query -results = kb.query([Query("Arctic Monkeys music genre"), - Query(text="U2 music genre", - top_k=10, - metadata_filter={"my-key": "my-value"})]) - -print(results[0].documents[0].text) -# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. -``` - -### Step 4: Create a context engine - -Context engine is an object that responsible to retrieve the most relevant context for a given query and token budget. -The context engine first uses the knowledge base to retrieve the most relevant documents. Then, it formalizes the textual context that will be presented to the LLM. This textual context might be structured or unstructured, depending on the use case and configuration. -The output of the context engine is designed to provide the LLM the most relevant context for a given query. - - -To create a context engine using a knowledge base, you can use the following command: - -```python -from resin.context_engine import ContextEngine -context_engine = ContextEngine(kb) -``` - -Then, you can use the `query` method to retrieve the most relevant context for a given query and token budget: - -```python -result = context_engine.query([Query("Arctic Monkeys music genre")], token_budget=100) - -print(result.content) -# output: Arctic Monkeys are an English rock band formed in Sheffield in 2002. - -print(result.token_count) -# output: 17 -``` - -By default, to handle the token budget constraint, the context engine will use the `StuffingContextBuilder` that will stuff as many documents as possible into the context without exceeding the token budget, by the order they have been retrieved from the knowledge base. - - -
-Go deeper -TBD -
- - -### Step 5: Create a chat engine - -Chat engine is an object that implements end to end chat API with [RAG](https://www.pinecone.io/learn/retrieval-augmented-generation/). -Given chat history, the chat engine orchestrates its underlying context engine and LLM to run the following steps: - -1. Generate search queries from the chat history -2. Retrieve the most relevant context for each query using the context engine -3. Prompt the LLM with the chat history and the retrieved context to generate the next response - -To create a chat engine using a context, you can use the following command: - -```python -from resin.chat_engine import ChatEngine -chat_engine = ChatEngine(context_engine) -``` - -Then, you can start chatting! - -```python -chat_engine.chat("what is the genre of Arctic Monkeys band?") -# output: Arctic Monkeys is a rock band. -``` - - -Resin designed to be production ready and handle any conversation length and context length. Therefore, the chat engine uses internal components to handle long conversations and long contexts. -By default, long chat history is truncated to the latest messages that fits the token budget. It orchestrates the context engine to retrieve context that fits the token budget and then use the LLM to generate the next response. - - -
-Go deeper -TBD -
- - - -## Understanding KnowledgeBase workings - -The knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. - -### Upsert workflow -The `upsert` method is used to insert of update textual documents of any size into the knowledge base. For each document, the following steps are performed: - -1. The document is chunked into smaller pieces of text, each piece is called a `Chunk`. -2. Each chunk is encoded into a vector representation. -3. The vector representation is inserted into the Pinecone index along with the document text and metadata. - -### Query workflow - -The `query` method is used to retrieve the most similar documents to a given query. For each query, the following steps are performed: -1. The query is encoded into a vector representation. -2. The vector representation is used to search the Pinecone index for the most similar vectors. -3. The retrieved vectors are decoded into `DocumentWithScore` objects and returned inside a `QueryResult` object. - -In the future, we intend to introduce a re-ranking phase to enhance the initial query outcomes. - -### KnowledgeBase components - -The knowledge base is composed of the following components: - -- **Index**: A Pinecone index that holds the vector representations of the documents. -- **Chunker**: A `Chunker` object that is used to chunk the documents into smaller pieces of text. -- **Encoder**: An `RecordEncoder` object that is used to encode the chunks and queries into vector representations. - -By default the knowledge base is initialized with `OpenAIRecordEncoder` which uses OpenAI embedding API to encode the text into vector representations, and `MarkdownChunker` which is based on a cloned version of Langchain's `MarkdownTextSplitter` [chunker](https://github.com/langchain-ai/langchain/blob/95a1b598fefbdb4c28db53e493d5f3242129a5f2/libs/langchain/langchain/text_splitter.py#L1374C7-L1374C27). - - -You can customize each component by passing any instance of `Chunker` or `RecordEncoder` to the `KnowledgeBase` constructor. -To read more about these classes please refer to the docstrings of `Chunker` and `RecordEncoder`. - From 875c8882af0a047f15ca70a496c67b91c6a3c6ea Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Tue, 24 Oct 2023 12:50:09 +0300 Subject: [PATCH 17/19] fix links --- docs/library.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/library.md b/docs/library.md index 3e4b0209..72208e56 100644 --- a/docs/library.md +++ b/docs/library.md @@ -1,12 +1,9 @@ # Resin Library -For most common use cases, users can simply deploy the fully-configurable [Resin service](add README chapter link here), which provides a REST API backend for your own RAG-infused Chatbot. +For most common use cases, users can simply deploy the fully-configurable [Resin service](../README.md), which provides a REST API backend for your own RAG-infused Chatbot. For advanced users, this page describes how to use `resin` core library directly to implement their own custom applications. - -> 💡 You can find notebooks with examples of how to use Resin library [here](../examples). - The idea behind Resin library is to provide a framework to build AI applications on top of Pinecone as a long memory storage for you own data. Resin library designed with the following principles in mind: - **Easy to use**: Resin is designed to be easy to use. It is well packaged and can be installed with a single command. @@ -18,7 +15,7 @@ The idea behind Resin library is to provide a framework to build AI applications ## High level architecture -![class architecture](https://github.com/pinecone-io/context-engine/blob/main/.readme-content/class_architecture.png) +![class architecture](../.readme-content/class_architecture.png) ## Installation From 9561db132dafa75e87b27b7cc39e9848ea689810 Mon Sep 17 00:00:00 2001 From: Amnon Catav Date: Tue, 24 Oct 2023 13:14:30 +0300 Subject: [PATCH 18/19] reference install from main readme --- docs/library.md | 45 +++------------------------------------------ 1 file changed, 3 insertions(+), 42 deletions(-) diff --git a/docs/library.md b/docs/library.md index 72208e56..8136adc2 100644 --- a/docs/library.md +++ b/docs/library.md @@ -17,50 +17,12 @@ The idea behind Resin library is to provide a framework to build AI applications ![class architecture](../.readme-content/class_architecture.png) -## Installation +## Setup -To install Resin, you only need to run a single command: - -```bash -pip install pinecone-resin -``` - -It is highly recommended to use a virtual environment for you project. You can read more about virtual environments [here](https://docs.python.org/3/tutorial/venv.html). +To setup resin, please follow the instructions [here](../README.md#setup). ## Quickstart - -### Step 0: Set up the environment variables - -Before you start using Resin, you need to set up the following environment variables: - -```bash -export PINECONE_API_KEY="" -export PINECONE_ENVIRONMENT="" -export OPENAI_API_KEY="" -``` - -or you can directly set them in your python code: - -```python -import os -os.environ["PINECONE_API_KEY"] = "" -os.environ["PINECONE_ENVIRONMENT"] = "" -os.environ["OPENAI_API_KEY"] = "" -``` - - -
- 👉 click here for more details - -| Name | Description | How to get it? | -|-----------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `PINECONE_API_KEY` | The API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search data | Register or log into your Pinecone account in the [console](https://app.pinecone.io/). You can access your API key from the "API Keys" section in the sidebar of your dashboard | -| `PINECONE_ENVIRONMENT`| Determines the Pinecone service cloud environment of your index e.g `west1-gcp`, `us-east-1-aws`, etc | You can find the Pinecone environment next to the API key in [console](https://app.pinecone.io/) | -| `OPENAI_API_KEY` | API key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat API | You can find your OpenAI API key [here](https://platform.openai.com/account/api-keys). You might need to login or register to OpenAI services | -
- - ### Step 1: Initialize global Tokenizer The `Tokenizer` object is used for converting text into tokens, which is the basic data represntation that is used for processing. @@ -148,7 +110,7 @@ You can always verify the connection to the Pinecone index with the `verify_inde kb.verify_index_connection() ``` -To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding_knowledgebase). +To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding-knowledgebase-workings). ### Step 3: Upsert and query data @@ -243,7 +205,6 @@ TBD
- ## Understanding KnowledgeBase workings The knowledge base is an object that is responsible for storing and query your data. It holds a connection to a single Pinecone index and provides a simple API to insert, delete and search textual documents. 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