Not getting answer for summarization

[deepak-hl]

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Example Code

def generate_embeddings(file_path=None,chunk_size=2000,chunk_overlap=100,persist_directory=None):
    if file_path:
        _, file_extension = os.path.splitext(file_path)
        file_extension = file_extension.lower()
        if file_path.lower().endswith(".pdf"):
            loader = PDFPlumberLoader(file_path)
            document = loader.load()
            # text_splitter = TokenTextSplitter(chunk_size=2000, chunk_overlap=200)
            text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
            texts = text_splitter.split_documents(documents=document)
        elif file_path.lower().endswith(".docx") or file_path.lower().endswith(".doc"):
            # Split DOCX text into chunks
            loader = Docx2txtLoader(file_path)
            document = loader.load()
            text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
            texts = text_splitter.split_documents(documents=document)
        elif file_path.lower().endswith(".xlsx"):
            loader = UnstructuredExcelLoader(file_path)
            docs = loader.load()
            text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
            texts = text_splitter.split_documents(documents=docs)
        else:
            pass

        print(AZURE_OPENAI_ENDPOINT,AZURE_OPENAI_API_KEY)

        embedding = AzureOpenAIEmbeddings(
                openai_api_version=AZURE_OPENAI_API_VERSION,
                openai_api_type='azure',
                openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"), 
                azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
                azure_deployment=os.getenv("DEPLOYMENT_NAME"),
                model="text-embedding-ada-002",
                validate_base_url=True
            )
        
        CONNECTION_STRING
        try:
            try:
                PGVector.from_texts(
                    embedding=embedding,
                    texts=texts,
                    collection_name=persist_directory,
                    connection_string=CONNECTION_STRING,
                )
            except:
                PGVector.from_documents(
                    embedding=embedding,
                    documents=texts,
                    collection_name=persist_directory,
                    connection_string=CONNECTION_STRING,
                )
        except Exception as e:
            print(e)
        file_crawl_status = True
    else:
        file_crawl_status = False

    return file_crawl_status


def generate_prompt():
    custom_prompt_template = """
        You are a summarization expert. Given the following detailed context, provide a clear, concise, and comprehensive summary that captures all the key points while maintaining the essence of the content:

        Context: {context}

        Your summary should be easy to understand and include all the main ideas, important details, and any conclusions or implications.

        Summary:
    """
    
    # Create the PromptTemplate without explicitly passing input_variables
    prompt = PromptTemplate.from_template(
        template=custom_prompt_template,
        template_format="f-string"  # Add template_format if needed
    )
    
    return prompt


def retrieval_qa(collection_name):
    prompt = generate_prompt()  # Ensure this returns a valid string or compatible object

    # Initialize embedding
    embedding = AzureOpenAIEmbeddings(
        openai_api_version=AZURE_OPENAI_API_VERSION,
        openai_api_type='azure',
        openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"), 
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
        azure_deployment=os.getenv("DEPLOYMENT_NAME"),
        model="text-embedding-ada-002",
        validate_base_url=True
    )
    
    # Initialize LLM
    try:
        llm = AzureChatOpenAI(
            azure_deployment="turbo",
            model="gpt-35-turbo",
            api_version="2023-06-01-preview",
            temperature=0,
            max_tokens=None,
            timeout=None,
            max_retries=2
        )
    except Exception as e:
        print(f"Error initializing LLM: {e}")
        return None

    # Set up the vector store
    try:
        vector_store = PGVector(
            connection_string=CONNECTION_STRING,
            collection_name=collection_name,
            embedding_function=embedding,
        )
    except Exception as e:
        print(f"Error initializing vector store: {e}")
        return None

    # Create the retriever
    try:
        retriever = vector_store.as_retriever(search_kwargs={"k": 200})
    except Exception as e:
        print(f"Error creating retriever: {e}")
        return None

    # Create the chain using map_reduce
    try:
        chain = RetrievalQA.from_chain_type(
            llm=llm,
            retriever=retriever,
            chain_type="map_reduce",
            return_source_documents=True,
            # prompt=prompt  # Ensure prompt is a string or compatible
        )
    except Exception as e:
        print(f"Error creating RetrievalQA chain: {e}")
        return None

    return chain



def process_pdf_summarization(file_path, collection_name):

    embedding_status = generate_embeddings(file_path=file_path, persist_directory=collection_name)

    if not embedding_status:
        raise ValueError("Failed to generate embeddings for the document.")
    print("stage 2")
    retriever = retrieval_qa(collection_name)
    print("stage 3")
    result = retriever.invoke({"query": "Summarize the document covering all aspects"})
    
    return result

Description

Not getting expected result and getting , change chain as per langchain v3

getting :
{'query': 'Summarize the document covering all aspects', 'result': "I'm sorry, but I cannot summarize the document without knowing which document you are referring to. Please provide more details or a specific question to answer.", 'source_documents': [Document(metadata={'source': 'RILLR19012024.pdf', 'file_path': 'RILLR19012024.pdf', 'page': 1, 'total_pages': 27, 'Author': 'Kavina Vora', 'Comments': '', 'Company': 'RIL', 'ContentTypeId': '0x0101006A3DA96ECCE9604EA1B796B38BC13BC3', 'CreationDate': "D:20240119180420+05'30'", 'Creator': 'Acrobat PDFMaker 23 for Word', 'Keywords': '', 'ModDate': "D:20240119181004+05'30'", 'Producer': 'Adobe PDF Library 23.8.234', 'SourceModified': '', 'Subject': '', 'Title': ''}, page_content='Regulations. Our responsibility is to express a conclusion on the Statement based on our\nreview.\n3. We conducted our review of the Statement in accordance wit

System Info

langchain
langchain-community
langchain-core
langchain-text-splitters
langchainhub
langdetect
langsmith
langchain-openai
pdfplumber
psycopg==3.1.18
psycopg-binary==3.1.18
psycopg2==2.9.9
psycopg2-binary==2.9.9
pgvector
openai==1.47.0

[dosubot[bot]]

I found a similar solved discussion that might help you with your issue: Retrieval QA and prompt templates. It discusses how to replace the default prompt template in RetrievalQAWithSourcesChain with a custom one.

Here are some steps and modifications you can make to your code to get the expected summarization result using LangChain v3:

1. Modify the Summarization Chain:
   Use the load_summarize_chain function and specify the appropriate chain type (stuff, map_reduce, or refine). Here is an example:

   from langchain_core.language_models import YourLanguageModel
   from langchain.chains import load_summarize_chain
   
   # Initialize your language model
   llm = YourLanguageModel()
   
   # Load the summarization chain
   summarize_chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=True)
   
   # Use the chain to summarize your documents
   documents = ["Your document text here."]
   summary = summarize_chain.run(documents)
   
   print(summary)

2. Handle Long Texts:
   Use the RecursiveCharacterTextSplitter to split your text into manageable chunks before summarization:

   from langchain_text_splitters import RecursiveCharacterTextSplitter
   
   chunk_size_tok = 2000
   text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
       chunk_size=chunk_size_tok, chunk_overlap=0
   )
   texts_split = text_splitter.split_text("Your large document text here.")

3. Create a Custom Summarization Chain:
   Use the create_stuff_documents_chain function, which supports better streaming and batch functionality:

   from langchain.chains.combine_documents import create_stuff_documents_chain
   from langchain_core.prompts import ChatPromptTemplate
   
   # Define the prompt template
   prompt = ChatPromptTemplate.from_template("Summarize this content: {context}")
   
   # Create the summarization chain
   chain = create_stuff_documents_chain(llm, prompt)
   
   # Use the chain to summarize your documents
   result = chain.invoke({"context": documents})
   print(result)

4. Streaming Output Tokens:
   If you need to process large documents, you can stream the output tokens:

   for chunk in chain.stream({"context": documents}):
       print(chunk, end=" | ")

5. Ensure Correct Prompt Usage:
   Make sure your custom prompt template includes the necessary input variables. For example:

   custom_prompt_template = """
       You are a summarization expert. Given the following detailed context, provide a clear, concise, and comprehensive summary that captures all the key points while maintaining the essence of the content:
   
       Context: {context}
   
       Your summary should be easy to understand and include all the main ideas, important details, and any conclusions or implications.
   
       Summary:
   """
   
   prompt = PromptTemplate.from_template(
       template=custom_prompt_template,
       template_format="f-string"
   )

By following these steps, you should be able to get the expected summarization result using LangChain v3. If you encounter any specific errors, please provide more details so I can assist you further.

For more details, you can refer to the relevant sources:

• LangChain Summarization Chain ^[1]
• LangChain Chains Initialization ^[2]
• LangChain Cookbook ^[3]
• LangChain Stuff Docs Chain ^[4]

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

To use the load_summarize_chain function with a custom prompt, you need to pass your custom prompt as an argument to the function. Here is an example of how to do this:

1. Import the necessary modules:

   from langchain_core.language_models import YourLanguageModel
   from langchain_core.prompts import PromptTemplate
   from langchain.chains.summarize.chain import load_summarize_chain

2. Initialize your language model:

   llm = YourLanguageModel()

3. Create your custom prompt template:

   custom_prompt_template = """
       You are a summarization expert. Given the following detailed context, provide a clear, concise, and comprehensive summary that captures all the key points while maintaining the essence of the content:
   
       Context: {context}
   
       Your summary should be easy to understand and include all the main ideas, important details, and any conclusions or implications.
   
       Summary:
   """
   custom_prompt = PromptTemplate(template=custom_prompt_template, input_variables=["context"])

4. Load the summarization chain with the custom prompt:

   summarize_chain = load_summarize_chain(
       llm=llm,
       chain_type="map_reduce",  # or "stuff" or "refine" depending on your use case
       prompt=custom_prompt,
       verbose=True
   )

5. Use the chain to summarize your documents:

   documents = ["Your document text here."]
   summary = summarize_chain(documents)
   print(summary)

Here is the complete code snippet:

from langchain_core.language_models import YourLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain.chains.summarize.chain import load_summarize_chain

# Initialize your language model
llm = YourLanguageModel()

# Create your custom prompt template
custom_prompt_template = """
    You are a summarization expert. Given the following detailed context, provide a clear, concise, and comprehensive summary that captures all the key points while maintaining the essence of the content:

    Context: {context}

    Your summary should be easy to understand and include all the main ideas, important details, and any conclusions or implications.

    Summary:
"""
custom_prompt = PromptTemplate(template=custom_prompt_template, input_variables=["context"])

# Load the summarization chain with the custom prompt
summarize_chain = load_summarize_chain(
    llm=llm,
    chain_type="map_reduce",  # or "stuff" or "refine" depending on your use case
    prompt=custom_prompt,
    verbose=True
)

# Use the chain to summarize your documents
documents = ["Your document text here."]
summary = summarize_chain(documents)

print(summary)

Make sure to replace YourLanguageModel with the actual class or function that initializes your language model. The chain_type parameter can be "stuff", "map_reduce", or "refine" depending on the summarization method you want to use ^[1][2].

To continue talking to Dosu, mention @dosu.

[deepak-hl]

@dosu can i fetch directly from the database instead of directly passing documnet here as on each call it will process the document

# Use the chain to summarize your documents
documents = ["Your document text here."]
summary = summarize_chain(documents)

[dosubot[bot]]

Yes, you can fetch documents directly from the database to avoid reprocessing them on each call. You can integrate a database retrieval step into the LangChain summarization process using a custom retriever and the create_retrieval_chain function. Here is an example:

1. Implement a custom retriever to fetch documents from your database:

   from langchain_core.retrievers import BaseRetriever, RetrieverOutput
   from langchain_core.documents import Document
   
   class DatabaseRetriever(BaseRetriever):
       def retrieve(self, input: dict) -> RetrieverOutput:
           # Implement your database retrieval logic here
           documents = fetch_documents_from_database(input["query"])
           return RetrieverOutput(documents=documents)
   
   def fetch_documents_from_database(query):
       # Replace this with your actual database fetching logic
       return [
           Document(page_content="Document content 1", metadata={"source": "source1"}),
           Document(page_content="Document content 2", metadata={"source": "source2"}),
       ]

2. Create the retrieval chain with your custom retriever and summarization chain:

   from langchain.chains import create_retrieval_chain
   from langchain.chains.combine_documents import create_stuff_documents_chain
   from langchain_core.prompts import PromptTemplate
   from langchain_community.chat_models import ChatOpenAI
   
   # Initialize your language model
   llm = ChatOpenAI(model_name="gpt-3.5-turbo")
   
   # Create your custom prompt template
   custom_prompt_template = """
       You are a summarization expert. Given the following detailed context, provide a clear, concise, and comprehensive summary that captures all the key points while maintaining the essence of the content:
   
       Context: {context}
   
       Your summary should be easy to understand and include all the main ideas, important details, and any conclusions or implications.
   
       Summary:
   """
   custom_prompt = PromptTemplate(template=custom_prompt_template, input_variables=["context"])
   
   # Create a summarization chain
   combine_docs_chain = create_stuff_documents_chain(llm, custom_prompt)
   
   # Create your custom retriever
   retriever = DatabaseRetriever()
   
   # Create the retrieval chain
   retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)
   
   # Invoke the chain with an input query
   result = retrieval_chain.invoke({"input": "Your query here"})
   print(result)

In this example:

• DatabaseRetriever is a custom retriever that fetches documents from your database.
• create_stuff_documents_chain is used to create a chain that combines the retrieved documents and processes them for summarization.
• create_retrieval_chain is used to create the retrieval chain that integrates the database retrieval step with the summarization process.

This setup ensures that documents are fetched from the database and processed for summarization without reprocessing them on each call ^[1][2].

To continue talking to Dosu, mention @dosu.

[deepak-hl]

@dosu

# Use the chain to summarize your documents
documents = ["Your document text here."]
summary = summarize_chain(documents)

print(summary)

how to fetch document with text processing

[dosubot[bot]]

To fetch a document with text processing and use it in the summarization chain, follow these steps:

1. Load the document:
   Use PyPDFLoader to load the document and split it into text chunks.

   from langchain_community.document_loaders import PyPDFLoader
   from langchain_text_splitters import RecursiveCharacterTextSplitter
   
   # Path to the document
   path = "/path/to/your/document/"
   loader = PyPDFLoader(path + "document.pdf")
   pdf_pages = loader.load()
   
   # Split the document into chunks
   text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
   all_splits_pypdf = text_splitter.split_documents(pdf_pages)
   all_splits_pypdf_texts = [d.page_content for d in all_splits_pypdf]

2. Summarize the text:
   Use a summarization chain to create concise summaries of the text chunks.

   from langchain_core.output_parsers import StrOutputParser
   from langchain_core.prompts import ChatPromptTemplate
   from langchain_openai import ChatOpenAI
   
   # Define the prompt for summarization
   prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \
   These summaries will be embedded and used to retrieve the raw text or table elements. \
   Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """
   prompt = ChatPromptTemplate.from_template(prompt_text)
   
   # Create the summarization chain
   model = ChatOpenAI(temperature=0, model="gpt-4")
   summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser()
   
   # Apply the summarization chain to the text chunks
   text_summaries = summarize_chain.batch(all_splits_pypdf_texts, {"max_concurrency": 5})

3. Store the summaries:
   Store the summarized text chunks in a vector store for retrieval.

   from langchain_chroma import Chroma
   from langchain_openai import OpenAIEmbeddings
   
   # Embed and store the text summaries
   baseline = Chroma.from_texts(
       texts=text_summaries,
       collection_name="baseline",
       embedding=OpenAIEmbeddings(),
   )
   retriever_baseline = baseline.as_retriever()

4. Build the RAG chain:
   Create a RAG (Retrieval-Augmented Generation) chain using the retriever.

   from langchain_core.runnables import RunnablePassthrough
   from langchain_core.prompts import ChatPromptTemplate
   from langchain_core.output_parsers import StrOutputParser
   from langchain_openai import ChatOpenAI
   
   # Define the RAG prompt
   template = """Answer the question based only on the following context, which can include text and tables:
   {context}
   Question: {question}
   """
   rag_prompt_text = ChatPromptTemplate.from_template(template)
   
   # Build the RAG chain
   def text_rag_chain(retriever):
       model = ChatOpenAI(temperature=0, model="gpt-4")
       chain = (
           {"context": retriever, "question": RunnablePassthrough()}
           | rag_prompt_text
           | model
           | StrOutputParser()
       )
       return chain
   
   # Create the RAG chain
   chain_baseline = text_rag_chain(retriever_baseline)

By following these steps, you can fetch a document, process its text, summarize it, and use it in a summarization chain ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.