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Installation

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

!pip install jq

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[?25hInstalling collected packages: jq
Successfully installed jq-1.6.0

!pip install git+https://github.com/aiplanethub/genai-stack.git

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Setup your API Key

import os
from getpass import getpass


api_key= ""
os.environ['OPENAI_API_KEY'] = ""

Import required modules

from genai_stack.stack.stack import Stack
from genai_stack.etl.langchain import LangchainETL
from genai_stack.embedding.langchain import LangchainEmbedding
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.prompt_engine.engine import PromptEngine
from genai_stack.model.gpt3_5 import OpenAIGpt35Model
from genai_stack.retriever.langchain import LangChainRetriever
from genai_stack.memory.langchain import ConversationBufferMemory

from genai_stack.etl.utils import get_config_from_source_kwargs
from genai_stack.embedding.utils import get_default_embeddings
from genai_stack.etl.langchain import LangchainETL
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.etl.utils import get_config_from_source_kwargs
from genai_stack.model.gpt3_5 import OpenAIGpt35Model

ETL - "Extract, Transform, and Load."

Add your data here. Check documentation for the required loaders

config = {
    "model_name": "sentence-transformers/all-mpnet-base-v2",
    "model_kwargs": {"device": "cpu"},
    "encode_kwargs": {"normalize_embeddings": False},
}



config = {
    "name": "JSONLoader",
    "fields": {
        "file_path": "/content/drive/MyDrive/genai/books.json",
         "jq_schema": '.[]',
        "content_key":"country",

    }
}



# Create ETL


etl =LangchainETL.from_kwargs(**config)

Create Default Embeddings

emd =get_default_embeddings()

Define your LLM - Large Language Model

# Create model
model = OpenAIGpt35Model.from_kwargs(
    parameters={"openai_api_key": ""} # Update with your OpenAI Key
)

Define The VectorDB

vb =ChromaDB.from_kwargs()

Connect the ETL, Embedding and Vectordb Component Using Stack


stack = Stack(model=model, embedding=emd, etl=etl, vectordb=vb)

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Run Your ETL


etl.run()

model.predict("In which language book Things Fall Apart book is ")

WARNING:langchain.llms.base:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..





{'output': 'Things Fall Apart is a novel written in English by Nigerian author Chinua Achebe.'}

Add Your Documents To Vectordb

# Add your documents
from langchain.docstore.document import Document as LangDocument

stack.vectordb.add_documents(
            documents=[
                LangDocument(
                    page_content="Some page content explaining something", metadata={"some_metadata": "some_metadata"}
                )
            ]
        )

['ed077608-68d4-11ee-90a7-0242ac1c000c']

Do Similarity Search In Vectordb

stack.vectordb.search("tell me name of author from india")

[Document(page_content='India', metadata={'seq_num': 94, 'source': '/content/drive/MyDrive/genai/books.json'}),
 Document(page_content='India', metadata={'seq_num': 51, 'source': '/content/drive/MyDrive/genai/books.json'}),
 Document(page_content='India', metadata={'seq_num': 96, 'source': '/content/drive/MyDrive/genai/books.json'}),
 Document(page_content='India', metadata={'seq_num': 51, 'source': '/content/drive/MyDrive/genai/books.json'})]

# query it
query = "tell me name of author from india"
docs = stack.vectordb.similarity_search(query)

# print results
print(docs[0].page_content)

India

PreviousChat on CSV NextDocument Search

Last updated 1 year ago