chore: function logic split into multiple files

functions/chain.py

@@ -0,0 +1,38 @@
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from meta import document_content_description, metadata_field_info
+
+
+def create_health_ai_chain(llm, vector_store):
+    retriever = SelfQueryRetriever.from_llm(
+        llm=llm,
+        vectorstore=vector_store,
+        document_content_description=document_content_description,
+        metadata_field_info=metadata_field_info,
+        document_contents='',
+    )
+    health_ai_template = """
+    You are a health AI agent equipped with access to diverse sources of health data,
+    including research articles, nutritional information, medical archives, and more.
+    Your task is to provide informed answers to user queries based on the available data.
+    If you cannot find relevant information, simply state that you do not have enough data
+    to answer accurately. write your response in markdown form and also add reference url
+    so user can know from which source you are answering the questions.
+
+    CONTEXT:
+    {context}
+
+    QUESTION: {question}
+
+    YOUR ANSWER:
+    """
+    health_ai_prompt = ChatPromptTemplate.from_template(health_ai_template)
+    chain = (
+        {'context': retriever, 'question': RunnablePassthrough()}
+        | health_ai_prompt
+        | llm
+        | StrOutputParser()
+    )
+    return chain

functions/config.py

@@ -0,0 +1,5 @@
+from firebase_admin import initialize_app
+
+
+def initialize_firebase():
+    initialize_app()

functions/handlers.py

@@ -0,0 +1,25 @@
+from os import environ
+
+from chain import create_health_ai_chain
+from config import initialize_firebase
+from langchain_openai import ChatOpenAI
+from store import get_vector_store
+
+initialize_firebase()
+
+
+def get_health_ai_response(question, llm):
+    vector_store = get_vector_store()
+    chain = create_health_ai_chain(llm, vector_store)
+    response = chain.invoke(question)
+    return response
+
+
+def get_response_from_llm(query, llm):
+    models = {'gpt-4': {}, 'gpt-3.5-turbo-instruct': {'name': 'gpt-3.5-turbo-instruct'}}
+    if llm in models:
+        llm_model = ChatOpenAI(api_key=environ.get('OPEN_AI_API_KEY'), temperature=0, **models[llm])
+        response = get_health_ai_response(query, llm_model)
+        return response
+    else:
+        return 'Model Not Found'

functions/main.py

@@ -1,187 +1,7 @@
 from json import dumps
 
-from firebase_admin import initialize_app
 from firebase_functions import https_fn, options
-from langchain.chains.query_constructor.base import AttributeInfo
-from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain_astradb import AstraDBVectorStore
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.runnables import RunnablePassthrough
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-
-initialize_app()
-
-
-# Initialize embeddings and vector store
-def initialize_vector_store(token):
-    embeddings = OpenAIEmbeddings(api_key='')
-    return AstraDBVectorStore(
-        embedding=embeddings,
-        collection_name='test_collection_2',
-        api_endpoint='',
-        token=token,
-        namespace='test',
-    )
-
-
-# Metadata field info
-metadata_field_info = [
-    AttributeInfo(
-        name='author',
-        description='The author of the YouTube video or nutrition article',
-        type='string',
-    ),
-    AttributeInfo(
-        name='videoId', description='The unique identifier for the YouTube video', type='string'
-    ),
-    AttributeInfo(name='title', description='The title of the content', type='string'),
-    AttributeInfo(
-        name='keywords',
-        description='A list of keywords associated with the YouTube video',
-        type='List[string]',
-    ),
-    AttributeInfo(
-        name='viewCount', description='The number of views for the YouTube video', type='string'
-    ),
-    AttributeInfo(
-        name='shortDescription',
-        description='A short description of the YouTube video',
-        type='string',
-    ),
-    AttributeInfo(name='transcript', description='The transcript of the content', type='string'),
-    AttributeInfo(
-        name='authors', description='The authors of the PubMed or Archive document', type='list'
-    ),
-    AttributeInfo(
-        name='publicationDate',
-        description='The publication date of the PubMed or Archive document',
-        type='string',
-    ),
-    AttributeInfo(
-        name='abstract', description='The abstract of the PubMed or Archive document', type='string'
-    ),
-    AttributeInfo(name='date', description='The date of the nutrition article', type='string'),
-    AttributeInfo(
-        name='keyPoints', description='The key points of the nutrition article', type='string'
-    ),
-    AttributeInfo(name='subTitle', description='The subtitle of the recipe', type='string'),
-    AttributeInfo(name='rating', description='The rating of the recipe', type='float'),
-    AttributeInfo(
-        name='recipeDetails',
-        description='The details of the recipe include the time also.',
-        type='Dict[string, string]',
-    ),
-    AttributeInfo(
-        name='ingredients', description='A list of ingredients for the recipe', type='List[string]'
-    ),
-    AttributeInfo(name='steps', description='The steps to prepare the recipe', type='List[string]'),
-    AttributeInfo(
-        name='nutritionFacts',
-        description='Nutritional facts of the recipe',
-        type='Dict[string, string]',
-    ),
-    AttributeInfo(
-        name='nutritionInfo',
-        description='Detailed nutritional information of the recipe',
-        type='Dict[string, Dict[string, string]]',
-    ),
-]
-
-document_content_description = """
-It includes a variety of metadata to describe different aspects of the content:
-
-General Information:
-- Title: The title of the content.
-- Transcript: A full transcript of any video, audio, or written content associated with document.
-
-YouTube Video Information:
-- Author: The author or creator of the YouTube video.
-- VideoId: The unique identifier for the YouTube video.
-- Keywords: A list of relevant keywords associated with the YouTube video.
-- ViewCount: The number of views for the YouTube video.
-- Short Description: A brief overview of the YouTube video.
-
-PubMed Article Information:
-- Authors: List of authors for the PubMed article.
-- PublicationDate: The date when the PubMed article was published.
-- Abstract: A summary of the PubMed article.
-
-Podcast Information:
-- Title: The title of the podcast episode.
-- Transcript: The transcript of the podcast episode.
-
-Nutrition Article Information:
-- Title: The title of the nutrition article.
-- Date: The date when the nutrition article was published.
-- Author: The author of the nutrition article.
-- Key Points: Important highlights or key points about recipe from the nutrition article.
-
-Recipe Information:
-- Title: The title of the recipe.
-- SubTitle: The subtitle of the recipe.
-- Rating: The rating of the recipe, if available.
-- Recipe Details: Detailed information about the recipe, including preparation time,
-cooking time, and serving size.
-- Ingredients: A list of ingredients required for making recipe.
-- Steps: Step-by-step instructions to prepare the dish.
-- Nutrition Facts: Basic nutritional information about the recipe.
-- Nutrition Info: Detailed nutritional information, including amounts and daily values.
-
-Archived Document Information:
-- Title: The title of the archived document.
-- Authors: List of authors for the archived document.
-- Abstract: A summary of the archived document.
-- PublicationDate: The date when the archived document was published.
-"""
-
-
-# Create a function to get response from the chain
-def get_health_ai_response(question, llm):
-    token = ''
-    vector_store = initialize_vector_store(token)
-    retriever = SelfQueryRetriever.from_llm(
-        llm=llm,
-        vectorstore=vector_store,
-        document_content_description=document_content_description,
-        metadata_field_info=metadata_field_info,
-        document_contents='',
-    )
-    health_ai_template = """
-    You are a health AI agent equipped with access to diverse sources of health data,
-    including research articles, nutritional information, medical archives, and more.
-    Your task is to provide informed answers to user queries based on the available data.
-    If you cannot find relevant information, simply state that you do not have enough data
-    to answer accurately. write your response in markdown form and also add reference url
-    so user can know from which source you are answering the questions.
-
-    CONTEXT:
-    {context}
-
-    QUESTION: {question}
-
-    YOUR ANSWER:
-    """
-    health_ai_prompt = ChatPromptTemplate.from_template(health_ai_template)
-    chain = (
-        {'context': retriever, 'question': RunnablePassthrough()}
-        | health_ai_prompt
-        | llm
-        | StrOutputParser()
-    )
-    response = chain.invoke(question)
-    return response
-
-
-def get_response_from_llm(query, llm):
-    api_key = ''
-    models = {'gpt-4': {}, 'gpt-3.5-turbo-instruct': {'name': 'gpt-3.5-turbo-instruct'}}
-    if llm in models:
-        llm_model = ChatOpenAI(api_key=api_key, temperature=0, **models[llm])
-        response = get_health_ai_response(query, llm_model)
-        return response
-    else:
-        return 'Model Not Found'
+from handlers import get_response_from_llm
 
 
 @https_fn.on_request(cors=options.CorsOptions(cors_origins=['*'], cors_methods=['get', 'post']))

functions/meta.py

@@ -0,0 +1,110 @@
+from langchain.chains.query_constructor.base import AttributeInfo
+
+metadata_field_info = [
+    AttributeInfo(
+        name='author',
+        description='The author of the YouTube video or nutrition article',
+        type='string',
+    ),
+    AttributeInfo(
+        name='videoId', description='The unique identifier for the YouTube video', type='string'
+    ),
+    AttributeInfo(name='title', description='The title of the content', type='string'),
+    AttributeInfo(
+        name='keywords',
+        description='A list of keywords associated with the YouTube video',
+        type='List[string]',
+    ),
+    AttributeInfo(
+        name='viewCount', description='The number of views for the YouTube video', type='string'
+    ),
+    AttributeInfo(
+        name='shortDescription',
+        description='A short description of the YouTube video',
+        type='string',
+    ),
+    AttributeInfo(name='transcript', description='The transcript of the content', type='string'),
+    AttributeInfo(
+        name='authors', description='The authors of the PubMed or Archive document', type='list'
+    ),
+    AttributeInfo(
+        name='publicationDate',
+        description='The publication date of the PubMed or Archive document',
+        type='string',
+    ),
+    AttributeInfo(
+        name='abstract', description='The abstract of the PubMed or Archive document', type='string'
+    ),
+    AttributeInfo(name='date', description='The date of the nutrition article', type='string'),
+    AttributeInfo(
+        name='keyPoints', description='The key points of the nutrition article', type='string'
+    ),
+    AttributeInfo(name='subTitle', description='The subtitle of the recipe', type='string'),
+    AttributeInfo(name='rating', description='The rating of the recipe', type='float'),
+    AttributeInfo(
+        name='recipeDetails',
+        description='The details of the recipe include the time also.',
+        type='Dict[string, string]',
+    ),
+    AttributeInfo(
+        name='ingredients', description='A list of ingredients for the recipe', type='List[string]'
+    ),
+    AttributeInfo(name='steps', description='The steps to prepare the recipe', type='List[string]'),
+    AttributeInfo(
+        name='nutritionFacts',
+        description='Nutritional facts of the recipe',
+        type='Dict[string, string]',
+    ),
+    AttributeInfo(
+        name='nutritionInfo',
+        description='Detailed nutritional information of the recipe',
+        type='Dict[string, Dict[string, string]]',
+    ),
+]
+
+document_content_description = """
+It includes a variety of metadata to describe different aspects of the content:
+
+General Information:
+- Title: The title of the content.
+- Transcript: A full transcript of any video, audio, or written content associated with document.
+
+YouTube Video Information:
+- Author: The author or creator of the YouTube video.
+- VideoId: The unique identifier for the YouTube video.
+- Keywords: A list of relevant keywords associated with the YouTube video.
+- ViewCount: The number of views for the YouTube video.
+- Short Description: A brief overview of the YouTube video.
+
+PubMed Article Information:
+- Authors: List of authors for the PubMed article.
+- PublicationDate: The date when the PubMed article was published.
+- Abstract: A summary of the PubMed article.
+
+Podcast Information:
+- Title: The title of the podcast episode.
+- Transcript: The transcript of the podcast episode.
+
+Nutrition Article Information:
+- Title: The title of the nutrition article.
+- Date: The date when the nutrition article was published.
+- Author: The author of the nutrition article.
+- Key Points: Important highlights or key points about recipe from the nutrition article.
+
+Recipe Information:
+- Title: The title of the recipe.
+- SubTitle: The subtitle of the recipe.
+- Rating: The rating of the recipe, if available.
+- Recipe Details: Detailed information about the recipe, including preparation time,
+cooking time, and serving size.
+- Ingredients: A list of ingredients required for making recipe.
+- Steps: Step-by-step instructions to prepare the dish.
+- Nutrition Facts: Basic nutritional information about the recipe.
+- Nutrition Info: Detailed nutritional information, including amounts and daily values.
+
+Archived Document Information:
+- Title: The title of the archived document.
+- Authors: List of authors for the archived document.
+- Abstract: A summary of the archived document.
+- PublicationDate: The date when the archived document was published.
+"""

functions/store.py

@@ -0,0 +1,16 @@
+from os import environ
+
+from langchain_astradb import AstraDBVectorStore
+from langchain_openai import OpenAIEmbeddings
+
+
+def get_vector_store():
+    embeddings = OpenAIEmbeddings(api_key=environ.get('OPEN_AI_API_KEY'))
+    astra_vector_store = AstraDBVectorStore(
+        embedding=embeddings,
+        collection_name=environ.get('ASTRA_DB_COLLECTION_NAME'),
+        api_endpoint=environ.get('ASTRA_DB_URL'),
+        token=environ.get('ASTRA_DB_TOKEN'),
+        namespace=environ.get('ASTRA_DB_NAMESPACE'),
+    )
+    return astra_vector_store