Update langchain_movie_search.py

Commented code to generate embeddings

langchain_movie_search.py

@@ -1,160 +1,160 @@
-import os
-from typing import List
-import argparse
-from dotenv import load_dotenv
-import pymongo
-from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings
-from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-from langchain.chains import create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.documents import Document
-from langchain_core.prompts import PromptTemplate
-import gradio as gr
-from gradio.themes.base import Base
-from flask import Flask
-
-__author__ = "Chirag Kamble"
-
-
-# Flask App
-# app = Flask(__name__)
-
-
-class MoviesSearch:
-    """
-    Class to perform Vector Index Search using MongoDB and LLM search using Langchain on Movies
-    """
-
-    def __init__(self):
-        """
-        Initializing method
-        """
-        # Load environment variables
-        load_dotenv()
-        transformer_model_name: str = os.getenv("TRANSFORMER_MODEL_NAME")
-        mongodb_connection_url: str = os.getenv("MONGODB_CONNECTION_URL")
-        mongodb_db_name: str = os.getenv("MONGODB_DB_NAME")
-        mongodb_collection_name: str = os.getenv("MONGODB_COLLECTION_NAME")
-        self.huggingface_repo: str = os.getenv("HF_REPO")
-        self.huggingface_api_token: str = os.getenv("HF_TOKEN")
-        self.huggingface_text_generation_model: str = os.getenv("HUGGINGFACE_TEXT_GENERATION_MODEL")
-
-        # Setup MongoDB connection
-        self.client: pymongo.synchronous.mongo_client.MongoClient = pymongo.MongoClient(mongodb_connection_url,
-                                                                                        serverSelectionTimeoutMS=50000)
-        db: str = mongodb_db_name
-        collection_name: str = mongodb_collection_name
-        self.langchain_movies_collection: pymongo.synchronous.collection.Collection = self.client[db][collection_name]
-
-        self.sample_movies_collection: pymongo.synchronous.collection.Collection = self.client.sample_mflix.movies
-
-        self.hf_plot_embedding = HuggingFaceEmbeddings(
-            model_name=transformer_model_name,
-            show_progress=True,
-        )
-
-        self.retrieve_vector_store = MongoDBAtlasVectorSearch(collection=self.langchain_movies_collection,
-                                                              embedding=self.hf_plot_embedding,
-                                                              embedding_key="embedding",
-                                                              index_name="langchain_movies_vector_index",
-                                                              text_key="text",
-                                                              )
-
-    def generate_insert_embeddings(self):
-        """
-        Generate vector embeddings
-        """
-        new_doc_list: List[Document] = []
-        for doc in self.sample_movies_collection.find({"fullplot": {"$exists": True}}).limit(1000):
-            new_doc: Document = Document(
-                page_content=doc["fullplot"],
-                metadata={"source": "Collection sample_mflix",
-                          "movie-title": doc["title"],
-                          "movie-plot": doc["fullplot"],
-                          "text": doc["fullplot"]}
-            )
-            new_doc_list.append(new_doc)
-        self.retrieve_vector_store.from_documents(
-            documents=new_doc_list,
-            embedding=self.hf_plot_embedding,
-            collection=self.langchain_movies_collection
-        )
-
-    def query_data(self, query: str):
-        """
-        Query data from Atlas Vector Search
-        :param query: A user query to search
-        :return: String answer generated by the LLM
-        """
-        hf_llm: HuggingFaceEndpoint = HuggingFaceEndpoint(
-            repo_id=self.huggingface_text_generation_model,
-            huggingfacehub_api_token=self.huggingface_api_token,
-            temperature=0.1,
-            task="text-generation",
-            # max_new_tokens=100,
-            verbose=True,
-            return_full_text=True,
-        )
-
-        retriever = self.retrieve_vector_store.as_retriever()
-        prompt = PromptTemplate.from_template(template="{context}", template_format="f-string")
-        combine_docs = create_stuff_documents_chain(llm=hf_llm, prompt=prompt, )
-
-        retrival_chain = create_retrieval_chain(retriever=retriever, combine_docs_chain=combine_docs)
-        hf_llm_retriever_output = retrival_chain.invoke({"input": query})
-
-        llm_answer = hf_llm_retriever_output.get("answer")
-
-        return llm_answer
-
-    def run_website(self):
-        with gr.Blocks(theme=Base(), title="Movie plot search App using Vector Search + RAG") as v_search:
-            gr.Markdown("Movie plot search App using Vector Search + RAG")
-            textbox = gr.Textbox(label="Enter your question:", lines=1)
-            with gr.Row():
-                button = gr.Button("Submit", variant="primary")
-            with gr.Column():
-                output = gr.Textbox(lines=1, max_lines=10, interactive=False,
-                                    label="""Output generated by chaining Atlas Vector Search with Langchain's RAG""",)
-
-            button.click(fn=self.query_data, inputs=textbox, outputs=[output])
-
-        v_search.launch(share=True)
-
-    def close_client(self):
-        self.client.close()
-
-
-# @app.route("/", methods=["GET"])
-def gradio_interface(cmd=None):
-    movie_search = MoviesSearch()
-    movie_search.generate_insert_embeddings()
-    movie_search.run_website()
-
-    # if cmd == "generate_embeddings":
-    #     movie_search.generate_insert_embeddings()
-    # elif cmd == "run":
-    #     movie_search.run_website()
-
-
-if __name__ == "__main__":
-    # Create the parser
-    # parser = argparse.ArgumentParser(description='Script to suggest movies based on user description/query')
-    #
-    # # Add arguments
-    # parser.add_argument("-g", "--generate_embeddings", action="store_true", help="Generate/Re-generate Embeddings")
-    # parser.add_argument("-r", "--run", action="store_true", help="Age of the person")
-    #
-    # # Parse arguments
-    # args = parser.parse_args()
-    #
-    # if args.generate_embeddings:
-    #     gradio_interface(cmd="generate_embeddings")
-    # elif args.run:
-    #     gradio_interface(cmd="run")
-
-    # app.run(host="0.0.0.0", port=os.getenv("PORT", 5000), debug=True)
-    # app.run(host="0.0.0.0", debug=True)
-    # app.run(debug=True)
-
-    gradio_interface()
+import os
+from typing import List
+import argparse
+from dotenv import load_dotenv
+import pymongo
+from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.documents import Document
+from langchain_core.prompts import PromptTemplate
+import gradio as gr
+from gradio.themes.base import Base
+from flask import Flask
+
+__author__ = "Chirag Kamble"
+
+
+# Flask App
+# app = Flask(__name__)
+
+
+class MoviesSearch:
+    """
+    Class to perform Vector Index Search using MongoDB and LLM search using Langchain on Movies
+    """
+
+    def __init__(self):
+        """
+        Initializing method
+        """
+        # Load environment variables
+        load_dotenv()
+        transformer_model_name: str = os.getenv("TRANSFORMER_MODEL_NAME")
+        mongodb_connection_url: str = os.getenv("MONGODB_CONNECTION_URL")
+        mongodb_db_name: str = os.getenv("MONGODB_DB_NAME")
+        mongodb_collection_name: str = os.getenv("MONGODB_COLLECTION_NAME")
+        self.huggingface_repo: str = os.getenv("HF_REPO")
+        self.huggingface_api_token: str = os.getenv("HF_TOKEN")
+        self.huggingface_text_generation_model: str = os.getenv("HUGGINGFACE_TEXT_GENERATION_MODEL")
+
+        # Setup MongoDB connection
+        self.client: pymongo.synchronous.mongo_client.MongoClient = pymongo.MongoClient(mongodb_connection_url,
+                                                                                        serverSelectionTimeoutMS=50000)
+        db: str = mongodb_db_name
+        collection_name: str = mongodb_collection_name
+        self.langchain_movies_collection: pymongo.synchronous.collection.Collection = self.client[db][collection_name]
+
+        self.sample_movies_collection: pymongo.synchronous.collection.Collection = self.client.sample_mflix.movies
+
+        self.hf_plot_embedding = HuggingFaceEmbeddings(
+            model_name=transformer_model_name,
+            show_progress=True,
+        )
+
+        self.retrieve_vector_store = MongoDBAtlasVectorSearch(collection=self.langchain_movies_collection,
+                                                              embedding=self.hf_plot_embedding,
+                                                              embedding_key="embedding",
+                                                              index_name="langchain_movies_vector_index",
+                                                              text_key="text",
+                                                              )
+
+    def generate_insert_embeddings(self):
+        """
+        Generate vector embeddings
+        """
+        new_doc_list: List[Document] = []
+        for doc in self.sample_movies_collection.find({"fullplot": {"$exists": True}}).limit(1000):
+            new_doc: Document = Document(
+                page_content=doc["fullplot"],
+                metadata={"source": "Collection sample_mflix",
+                          "movie-title": doc["title"],
+                          "movie-plot": doc["fullplot"],
+                          "text": doc["fullplot"]}
+            )
+            new_doc_list.append(new_doc)
+        self.retrieve_vector_store.from_documents(
+            documents=new_doc_list,
+            embedding=self.hf_plot_embedding,
+            collection=self.langchain_movies_collection
+        )
+
+    def query_data(self, query: str):
+        """
+        Query data from Atlas Vector Search
+        :param query: A user query to search
+        :return: String answer generated by the LLM
+        """
+        hf_llm: HuggingFaceEndpoint = HuggingFaceEndpoint(
+            repo_id=self.huggingface_text_generation_model,
+            huggingfacehub_api_token=self.huggingface_api_token,
+            temperature=0.1,
+            task="text-generation",
+            # max_new_tokens=100,
+            verbose=True,
+            return_full_text=True,
+        )
+
+        retriever = self.retrieve_vector_store.as_retriever()
+        prompt = PromptTemplate.from_template(template="{context}", template_format="f-string")
+        combine_docs = create_stuff_documents_chain(llm=hf_llm, prompt=prompt, )
+
+        retrival_chain = create_retrieval_chain(retriever=retriever, combine_docs_chain=combine_docs)
+        hf_llm_retriever_output = retrival_chain.invoke({"input": query})
+
+        llm_answer = hf_llm_retriever_output.get("answer")
+
+        return llm_answer
+
+    def run_website(self):
+        with gr.Blocks(theme=Base(), title="Movie plot search App using Vector Search + RAG") as v_search:
+            gr.Markdown("Movie plot search App using Vector Search + RAG")
+            textbox = gr.Textbox(label="Enter your question:", lines=1)
+            with gr.Row():
+                button = gr.Button("Submit", variant="primary")
+            with gr.Column():
+                output = gr.Textbox(lines=1, max_lines=10, interactive=False,
+                                    label="""Output generated by chaining Atlas Vector Search with Langchain's RAG""",)
+
+            button.click(fn=self.query_data, inputs=textbox, outputs=[output])
+
+        v_search.launch(share=True)
+
+    def close_client(self):
+        self.client.close()
+
+
+# @app.route("/", methods=["GET"])
+def gradio_interface(cmd=None):
+    movie_search = MoviesSearch()
+    # movie_search.generate_insert_embeddings()
+    movie_search.run_website()
+
+    # if cmd == "generate_embeddings":
+    #     movie_search.generate_insert_embeddings()
+    # elif cmd == "run":
+    #     movie_search.run_website()
+
+
+if __name__ == "__main__":
+    # Create the parser
+    # parser = argparse.ArgumentParser(description='Script to suggest movies based on user description/query')
+    #
+    # # Add arguments
+    # parser.add_argument("-g", "--generate_embeddings", action="store_true", help="Generate/Re-generate Embeddings")
+    # parser.add_argument("-r", "--run", action="store_true", help="Age of the person")
+    #
+    # # Parse arguments
+    # args = parser.parse_args()
+    #
+    # if args.generate_embeddings:
+    #     gradio_interface(cmd="generate_embeddings")
+    # elif args.run:
+    #     gradio_interface(cmd="run")
+
+    # app.run(host="0.0.0.0", port=os.getenv("PORT", 5000), debug=True)
+    # app.run(host="0.0.0.0", debug=True)
+    # app.run(debug=True)
+
+    gradio_interface()