Update src/utils/ingest_text.py

src/utils/ingest_text.py

@@ -1,116 +1,116 @@
-from llama_parse import LlamaParse
-from langchain_chroma import Chroma
-from qdrant_client import QdrantClient
-from langchain_community.vectorstores.qdrant import Qdrant
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-from langchain_community.document_loaders.directory import DirectoryLoader
-import os
-from fastembed import TextEmbedding
-from typing import List
-
-import nest_asyncio
-nest_asyncio.apply()
-
-llamaparse_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
-#qdrant_url = os.getenv("QDRANT_URL ")
-#qdrant_api_key = os.getenv("QDRANT_API_KEY")
-groq_api_key = os.getenv("GROQ_API_KEY")
-
-
-parsed_data_file = r"C:\Users\Naresh Kumar Lahajal\Desktop\multimodal\data\parsed_data.pkl"
-output_md = r"C:\Users\Naresh Kumar Lahajal\Desktop\multimodal\data\output.md"
-loki = r"C:\Users\Naresh Kumar Lahajal\Desktop\multimodal\data"
-
-import pickle
-# Define a function to load parsed data if available, or parse if not
-def load_or_parse_data(loc):
-    data_file = parsed_data_file
-
-    if os.path.exists(data_file):
-        # Load the parsed data from the file
-        with open(data_file, "rb") as f:
-            parsed_data = pickle.load(f)
-    else:
-        # Perform the parsing step and store the result in llama_parse_documents
-        parsingInstructiontest10k = """The provided document is an user guide or a manual.
-        It contains many images and tables.
-        Try to be precise while answering the questions"""
-        parser = LlamaParse(api_key=llamaparse_api_key, result_type="markdown", parsing_instruction=parsingInstructiontest10k) # type: ignore
-        llama_parse_documents = parser.load_data(loc)
-
-
-        # Save the parsed data to a file
-        with open(data_file, "wb") as f:
-            pickle.dump(llama_parse_documents, f)
-
-        # Set the parsed data to the variable
-        parsed_data = llama_parse_documents
-
-    return parsed_data
-
-
-# Create vector database
-def create_vector_database(loc):
-    """
-    Creates a vector database using document loaders and embeddings.
-
-    This function loads urls,
-    splits the loaded documents into chunks, transforms them into embeddings using OllamaEmbeddings,
-    and finally persists the embeddings into a Chroma vector database.
-
-    """
-    # Call the function to either load or parse the data
-    llama_parse_documents = load_or_parse_data(loc)
-    #print(llama_parse_documents[1].text[:100])
-
-    #with open('data/output.md', 'a') as f:  # Open the file in append mode ('a')
-      #  for doc in llama_parse_documents:
-       #     f.write(doc.text + '\n')
-    with open(output_md,'a', encoding='utf-8') as f:  # Open the file in append mode ('a')
-        for doc in llama_parse_documents:
-            f.write(doc.text + '\n')
-
-    loader = DirectoryLoader(loki, glob="**/*.md", show_progress=True)
-    documents = loader.load()
-    # Split loaded documents into chunks
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=100)
-    print('data chunckex')
-    docs = text_splitter.split_documents(documents)
-    print(len(docs))
-
-    #len(docs)
-    #docs[0]
-
-    # Initialize Embeddings
-    embeddings = FastEmbedEmbeddings() # type: ignore
-    #embeddings = TextEmbedding()
-
-    print('Vector DB started!')
-
-    # Create and persist a Chroma vector database from the chunked documents
-    qdrant = Qdrant.from_documents(
-        documents=docs,
-        embedding=embeddings,
-        path="local_qdrant",
-        #url=qdrant_url,
-        collection_name="rag"
-        #api_key=qdrant_api_key
-    )
-    # save to disk
-    #db2 = Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db")
-    #docs = db2.similarity_search(query)
-
-# load from disk
-    #db3 = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
-
-    #query it
-    #query = "what is the agend of Financial Statements for 2022 ?"
-    #found_doc = qdrant.similarity_search(query, k=3)
-    #print(found_doc[0][:100])
-    #
-    print('Vector DB created successfully !')
-    #query = "Switching between external devices connected to the TV"
-    #found_doc = qdrant.similarity_search(query, k=3)
-    #print(found_doc)
+from llama_parse import LlamaParse
+from langchain_chroma import Chroma
+from qdrant_client import QdrantClient
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
+from langchain_community.document_loaders.directory import DirectoryLoader
+import os
+from fastembed import TextEmbedding
+from typing import List
+
+import nest_asyncio
+nest_asyncio.apply()
+
+llamaparse_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
+#qdrant_url = os.getenv("QDRANT_URL ")
+#qdrant_api_key = os.getenv("QDRANT_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+
+parsed_data_file = r".\data\parsed_data.pkl"
+output_md = r".\data\output.md"
+loki = r".\data"
+
+import pickle
+# Define a function to load parsed data if available, or parse if not
+def load_or_parse_data(loc):
+    data_file = parsed_data_file
+
+    if os.path.exists(data_file):
+        # Load the parsed data from the file
+        with open(data_file, "rb") as f:
+            parsed_data = pickle.load(f)
+    else:
+        # Perform the parsing step and store the result in llama_parse_documents
+        parsingInstructiontest10k = """The provided document is an user guide or a manual.
+        It contains many images and tables.
+        Try to be precise while answering the questions"""
+        parser = LlamaParse(api_key=llamaparse_api_key, result_type="markdown", parsing_instruction=parsingInstructiontest10k) # type: ignore
+        llama_parse_documents = parser.load_data(loc)
+
+
+        # Save the parsed data to a file
+        with open(data_file, "wb") as f:
+            pickle.dump(llama_parse_documents, f)
+
+        # Set the parsed data to the variable
+        parsed_data = llama_parse_documents
+
+    return parsed_data
+
+
+# Create vector database
+def create_vector_database(loc):
+    """
+    Creates a vector database using document loaders and embeddings.
+
+    This function loads urls,
+    splits the loaded documents into chunks, transforms them into embeddings using OllamaEmbeddings,
+    and finally persists the embeddings into a Chroma vector database.
+
+    """
+    # Call the function to either load or parse the data
+    llama_parse_documents = load_or_parse_data(loc)
+    #print(llama_parse_documents[1].text[:100])
+
+    #with open('data/output.md', 'a') as f:  # Open the file in append mode ('a')
+      #  for doc in llama_parse_documents:
+       #     f.write(doc.text + '\n')
+    with open(output_md,'a', encoding='utf-8') as f:  # Open the file in append mode ('a')
+        for doc in llama_parse_documents:
+            f.write(doc.text + '\n')
+
+    loader = DirectoryLoader(loki, glob="**/*.md", show_progress=True)
+    documents = loader.load()
+    # Split loaded documents into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=100)
+    print('data chunckex')
+    docs = text_splitter.split_documents(documents)
+    print(len(docs))
+
+    #len(docs)
+    #docs[0]
+
+    # Initialize Embeddings
+    embeddings = FastEmbedEmbeddings() # type: ignore
+    #embeddings = TextEmbedding()
+
+    print('Vector DB started!')
+
+    # Create and persist a Chroma vector database from the chunked documents
+    qdrant = Qdrant.from_documents(
+        documents=docs,
+        embedding=embeddings,
+        path="local_qdrant",
+        #url=qdrant_url,
+        collection_name="rag"
+        #api_key=qdrant_api_key
+    )
+    # save to disk
+    #db2 = Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db")
+    #docs = db2.similarity_search(query)
+
+# load from disk
+    #db3 = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+
+    #query it
+    #query = "what is the agend of Financial Statements for 2022 ?"
+    #found_doc = qdrant.similarity_search(query, k=3)
+    #print(found_doc[0][:100])
+    #
+    print('Vector DB created successfully !')
+    #query = "Switching between external devices connected to the TV"
+    #found_doc = qdrant.similarity_search(query, k=3)
+    #print(found_doc)
     return qdrant