minor changes

LLM.py

@@ -67,6 +67,6 @@ if __name__=="__main__":
    ]
    context=get_context_for_questions(questions)
    from chunking import word_count
-   print(word_count(context[0]))
-   #prompts=construct_prompts(questions,context)
-   #print(generate_answers(prompts))
+   #print(word_count(context[0]))
+   prompts=construct_prompts(questions,context)
+   print(generate_answers(prompts))

chunking.py

@@ -1,3 +1,157 @@
+# import re
+# from typing import List
+
+
+# def word_count(text: str) -> int:
+#     return len(text.strip().split())
+
+
+# def split_paragraphs(text: str) -> List[str]:
+#     """
+#     Split text by double newlines assuming paragraphs separated by blank lines.
+#     """
+#     paras = [p.strip() for p in text.split('\n\n') if p.strip()]
+#     return paras
+
+
+# def split_content_into_batches(
+#     content: str,
+#     max_words: int = 2000,
+#     batch_size: int = 16,
+#     overlap_words: int = 100  # number of words to overlap between chunk boundaries
+# ):
+#     """
+#     Splits content by # (level 1), then groups level 2 subsections (##) into chunks with max_words,
+#     overlaps last subsection of previous chunk into next chunk, and batches chunks into batch_size.
+
+#     Prints the word count of final chunks and how many chunks were split by each level.
+
+#     Returns:
+#         List of batches, each is a list of chunk strings.
+#     """
+
+#     level_1_pattern = re.compile(r'^# (.+)', re.MULTILINE)
+#     level_2_pattern = re.compile(r'^## (.+)', re.MULTILINE)
+
+#     level_1_indexes = [(m.start(), m.end(), m.group(1)) for m in level_1_pattern.finditer(content)]
+#     ends = [pos[0] for pos in level_1_indexes[1:]] + [len(content)]
+
+#     chunks = []
+
+#     # Counters for debugging
+#     count_level_1_chunks = 0
+#     count_level_2_chunks = 0  # will count grouped chunks here
+#     count_paragraph_chunks = 0
+
+#     def get_word_slice(text: str, word_limit: int, from_end=False) -> str:
+#         """
+#         Utility to return first/last `word_limit` words of text.
+#         """
+#         words = text.strip().split()
+#         if from_end:
+#             return " ".join(words[-word_limit:])
+#         else:
+#             return " ".join(words[:word_limit])
+
+#     for i, (start, heading_end, heading_text) in enumerate(level_1_indexes):
+#         section_start = heading_end
+#         section_end = ends[i]
+#         section_text = content[section_start:section_end].strip()
+
+#         section_wc = word_count(section_text)
+
+#         if section_wc <= max_words:
+#             # Whole section fits in one chunk, no grouping needed
+#             final_chunk = f"# {heading_text}\n{section_text}"
+#             chunks.append(final_chunk)
+#             print(f"Final chunk word count: {word_count(final_chunk)}")
+#             count_level_1_chunks += 1
+#             continue
+
+#         # Get all level 2 subsections within this level 1 section
+#         level_2_indexes = [(m.start(), m.end(), m.group(1)) for m in level_2_pattern.finditer(section_text)]
+#         if not level_2_indexes:
+#             # No subsections, fallback: split by paragraphs
+#             paragraphs = split_paragraphs(section_text)
+#             para_chunks = []
+#             current_chunk = ""
+#             for para in paragraphs:
+#                 if word_count(current_chunk) + word_count(para) + 1 <= max_words:
+#                     current_chunk += "\n\n" + para if current_chunk else para
+#                 else:
+#                     para_chunks.append(current_chunk)
+#                     current_chunk = para
+#             if current_chunk:
+#                 para_chunks.append(current_chunk)
+#             for pc in para_chunks:
+#                 final_chunk = f"# {heading_text}\n{pc}"
+#                 chunks.append(final_chunk)
+#                 print(f"Final chunk word count: {word_count(final_chunk)}")
+#                 count_paragraph_chunks += 1
+#             continue
+
+#         # Otherwise, group multiple level 2 subsections until max_words limit reached
+#         level_2_ends = [pos[0] for pos in level_2_indexes[1:]] + [len(section_text)]
+
+#         grouped_chunks = []
+#         current_chunk_subsections = []
+#         current_chunk_word_count = 0
+
+#         for j, (l2_start, l2_end, l2_heading_text) in enumerate(level_2_indexes):
+#             subsec_start = l2_end
+#             subsec_end = level_2_ends[j]
+#             subsec_text = section_text[subsec_start:subsec_end].strip()
+#             subsec_wc = word_count(subsec_text)
+
+#             # Prepare full subsection text (heading + content)
+#             full_subsec_text = f"## {l2_heading_text}\n{subsec_text}"
+
+#             # If adding this subsection exceeds limit, yield current chunk and start new chunk
+#             if current_chunk_word_count + subsec_wc > max_words and current_chunk_subsections:
+#                 # Form chunk text concatenating all subsections added so far
+#                 chunk_text = "\n\n".join(current_chunk_subsections)
+#                 # Prepend level 1 heading
+#                 chunk_text = f"# {heading_text}\n{chunk_text}"
+#                 grouped_chunks.append(chunk_text)
+#                 print(f"Final chunk word count: {word_count(chunk_text)}")
+#                 count_level_2_chunks += 1
+
+#                 # Prepare overlap: repeat last subsection (or last part of it) in next chunk
+#                 overlap_text = current_chunk_subsections[-1]
+#                 overlap_words_text = get_word_slice(overlap_text, overlap_words, from_end=True)
+#                 overlap_chunk_text = f"## {l2_heading_text}\n{overlap_words_text}"
+
+#                 # Start new chunk with overlap
+#                 current_chunk_subsections = [overlap_chunk_text]
+#                 current_chunk_word_count = word_count(overlap_words_text)
+
+#                 # Add current subsection fresh after overlap
+#                 current_chunk_subsections.append(full_subsec_text)
+#                 current_chunk_word_count += subsec_wc
+
+#             else:
+#                 # Add subsection to current chunk normally
+#                 current_chunk_subsections.append(full_subsec_text)
+#                 current_chunk_word_count += subsec_wc
+
+#         # Add the last chunk if any
+#         if current_chunk_subsections:
+#             chunk_text = "\n\n".join(current_chunk_subsections)
+#             chunk_text = f"# {heading_text}\n{chunk_text}"
+#             grouped_chunks.append(chunk_text)
+#             print(f"Final chunk word count: {word_count(chunk_text)}")
+#             count_level_2_chunks += 1
+
+#         chunks.extend(grouped_chunks)
+
+#     batches = [chunks[i:i + batch_size] for i in range(0, len(chunks), batch_size)]
+
+#     print(f"Chunks split by level 1 headings (#): {count_level_1_chunks}")
+#     print(f"Chunks split by grouped level 2 headings (##): {count_level_2_chunks}")
+#     print(f"Chunks split by paragraphs: {count_paragraph_chunks}")
+
+#     return batches
+
 import re
 from typing import List
 
@@ -10,28 +164,67 @@ def split_paragraphs(text: str) -> List[str]:
     """
     Split text by double newlines assuming paragraphs separated by blank lines.
     """
-    paras = [p.strip() for p in text.split('\n\n') if p.strip()]
+    paras = [p.strip() for p in text.split("\n\n") if p.strip()]
     return paras
 
 
+def split_to_fit(text: str, max_words: int) -> List[str]:
+    """
+    Ensure text is broken into chunks each <= max_words by paragraph first, then sliding window.
+    """
+    words = text.strip().split()
+    if len(words) <= max_words:
+        return [" ".join(words)]
+    # Try paragraph-level
+    paras = split_paragraphs(text)
+    chunks = []
+    current = []
+    for para in paras:
+        para_words = para.strip().split()
+        if sum(len(p.split()) for p in current) + len(para_words) <= max_words:
+            current.append(para)
+        else:
+            if current:
+                chunks.append(" ".join(current))
+            # if single paragraph too big, fallback to word sliding
+            if len(para_words) > max_words:
+                step = max_words
+                for i in range(0, len(para_words), step):
+                    part = " ".join(para_words[i : i + max_words])
+                    chunks.append(part)
+                current = []
+            else:
+                current = [para]
+    if current:
+        chunks.append(" ".join(current))
+    # As a final fallback, split any remaining oversize
+    final = []
+    for c in chunks:
+        if word_count(c) <= max_words:
+            final.append(c)
+        else:
+            wc_words = c.strip().split()
+            step = max_words
+            for i in range(0, len(wc_words), step):
+                final.append(" ".join(wc_words[i : i + max_words]))
+    return final
+
+
 def split_content_into_batches(
     content: str,
     max_words: int = 2000,
     batch_size: int = 16,
-    overlap_words: int = 100  # number of words to overlap between chunk boundaries
+    overlap_words: int = 200,  # number of words to overlap between chunk boundaries
 ):
     """
     Splits content by # (level 1), then groups level 2 subsections (##) into chunks with max_words,
     overlaps last subsection of previous chunk into next chunk, and batches chunks into batch_size.
-
-    Prints the word count of final chunks and how many chunks were split by each level.
-
-    Returns:
-        List of batches, each is a list of chunk strings.
+    Enforces strict cap: no chunk exceeds max_words.
+    Prints final word count of each chunk.
     """
 
-    level_1_pattern = re.compile(r'^# (.+)', re.MULTILINE)
-    level_2_pattern = re.compile(r'^## (.+)', re.MULTILINE)
+    level_1_pattern = re.compile(r"^# (.+)", re.MULTILINE)
+    level_2_pattern = re.compile(r"^## (.+)", re.MULTILINE)
 
     level_1_indexes = [(m.start(), m.end(), m.group(1)) for m in level_1_pattern.finditer(content)]
     ends = [pos[0] for pos in level_1_indexes[1:]] + [len(content)]
@@ -40,13 +233,10 @@ def split_content_into_batches(
 
     # Counters for debugging
     count_level_1_chunks = 0
-    count_level_2_chunks = 0  # will count grouped chunks here
+    count_level_2_chunks = 0
     count_paragraph_chunks = 0
 
     def get_word_slice(text: str, word_limit: int, from_end=False) -> str:
-        """
-        Utility to return first/last `word_limit` words of text.
-        """
         words = text.strip().split()
         if from_end:
             return " ".join(words[-word_limit:])
@@ -61,93 +251,90 @@ def split_content_into_batches(
         section_wc = word_count(section_text)
 
         if section_wc <= max_words:
-            # Whole section fits in one chunk, no grouping needed
             final_chunk = f"# {heading_text}\n{section_text}"
             chunks.append(final_chunk)
-            print(f"Final chunk word count: {word_count(final_chunk)}")
             count_level_1_chunks += 1
             continue
 
-        # Get all level 2 subsections within this level 1 section
         level_2_indexes = [(m.start(), m.end(), m.group(1)) for m in level_2_pattern.finditer(section_text)]
         if not level_2_indexes:
-            # No subsections, fallback: split by paragraphs
+            # Fallback: split by paragraphs, enforcing cap
             paragraphs = split_paragraphs(section_text)
             para_chunks = []
             current_chunk = ""
             for para in paragraphs:
-                if word_count(current_chunk) + word_count(para) + 1 <= max_words:
-                    current_chunk += "\n\n" + para if current_chunk else para
+                candidate = f"{current_chunk}\n\n{para}" if current_chunk else para
+                if word_count(candidate) <= max_words:
+                    current_chunk = candidate
                 else:
-                    para_chunks.append(current_chunk)
+                    if current_chunk:
+                        para_chunks.extend(split_to_fit(current_chunk, max_words))
                     current_chunk = para
             if current_chunk:
-                para_chunks.append(current_chunk)
+                para_chunks.extend(split_to_fit(current_chunk, max_words))
             for pc in para_chunks:
                 final_chunk = f"# {heading_text}\n{pc}"
                 chunks.append(final_chunk)
-                print(f"Final chunk word count: {word_count(final_chunk)}")
                 count_paragraph_chunks += 1
             continue
 
-        # Otherwise, group multiple level 2 subsections until max_words limit reached
         level_2_ends = [pos[0] for pos in level_2_indexes[1:]] + [len(section_text)]
 
-        grouped_chunks = []
         current_chunk_subsections = []
         current_chunk_word_count = 0
 
         for j, (l2_start, l2_end, l2_heading_text) in enumerate(level_2_indexes):
             subsec_start = l2_end
             subsec_end = level_2_ends[j]
-            subsec_text = section_text[subsec_start:subsec_end].strip()
-            subsec_wc = word_count(subsec_text)
-
-            # Prepare full subsection text (heading + content)
-            full_subsec_text = f"## {l2_heading_text}\n{subsec_text}"
-
-            # If adding this subsection exceeds limit, yield current chunk and start new chunk
-            if current_chunk_word_count + subsec_wc > max_words and current_chunk_subsections:
-                # Form chunk text concatenating all subsections added so far
-                chunk_text = "\n\n".join(current_chunk_subsections)
-                # Prepend level 1 heading
-                chunk_text = f"# {heading_text}\n{chunk_text}"
-                grouped_chunks.append(chunk_text)
-                print(f"Final chunk word count: {word_count(chunk_text)}")
-                count_level_2_chunks += 1
-
-                # Prepare overlap: repeat last subsection (or last part of it) in next chunk
-                overlap_text = current_chunk_subsections[-1]
-                overlap_words_text = get_word_slice(overlap_text, overlap_words, from_end=True)
-                overlap_chunk_text = f"## {l2_heading_text}\n{overlap_words_text}"
-
-                # Start new chunk with overlap
-                current_chunk_subsections = [overlap_chunk_text]
-                current_chunk_word_count = word_count(overlap_words_text)
-
-                # Add current subsection fresh after overlap
-                current_chunk_subsections.append(full_subsec_text)
-                current_chunk_word_count += subsec_wc
+            raw_subsec_text = section_text[subsec_start:subsec_end].strip()
 
-            else:
-                # Add subsection to current chunk normally
-                current_chunk_subsections.append(full_subsec_text)
-                current_chunk_word_count += subsec_wc
+            # ensure subsections themselves don't violate cap
+            subsec_pieces = split_to_fit(raw_subsec_text, max_words)
+            for piece in subsec_pieces:
+                piece_wc = word_count(piece)
+                heading_with_piece = f"## {l2_heading_text}\n{piece}"
+
+                if current_chunk_word_count + piece_wc > max_words and current_chunk_subsections:
+                    chunk_text = "\n\n".join(current_chunk_subsections)
+                    chunk_text = f"# {heading_text}\n{chunk_text}"
+                    for final_piece in split_to_fit(chunk_text, max_words):
+                        chunks.append(final_piece)
+                    count_level_2_chunks += 1
+
+                    # overlap: take last subsection (or piece) and include tail
+                    overlap_text = current_chunk_subsections[-1]
+                    overlap_words_text = get_word_slice(overlap_text, overlap_words, from_end=True)
+                    current_chunk_subsections = [overlap_words_text]
+                    current_chunk_word_count = word_count(overlap_words_text)
+
+                    current_chunk_subsections.append(heading_with_piece)
+                    current_chunk_word_count += piece_wc
+                else:
+                    current_chunk_subsections.append(heading_with_piece)
+                    current_chunk_word_count += piece_wc
 
-        # Add the last chunk if any
         if current_chunk_subsections:
             chunk_text = "\n\n".join(current_chunk_subsections)
             chunk_text = f"# {heading_text}\n{chunk_text}"
-            grouped_chunks.append(chunk_text)
-            print(f"Final chunk word count: {word_count(chunk_text)}")
+            for final_piece in split_to_fit(chunk_text, max_words):
+                chunks.append(final_piece)
             count_level_2_chunks += 1
 
-        chunks.extend(grouped_chunks)
+    # Final batching
+    batches = [chunks[i : i + batch_size] for i in range(0, len(chunks), batch_size)]
 
-    batches = [chunks[i:i + batch_size] for i in range(0, len(chunks), batch_size)]
+    # Print final word counts per chunk
+    for bi, batch in enumerate(batches, start=1):
+        print(f"\nBatch {bi}:")
+        for ci, chunk in enumerate(batch, start=1):
+            wc = word_count(chunk)
+            print(f"  Chunk {ci} word count: {wc}")
 
-    print(f"Chunks split by level 1 headings (#): {count_level_1_chunks}")
-    print(f"Chunks split by grouped level 2 headings (##): {count_level_2_chunks}")
-    print(f"Chunks split by paragraphs: {count_paragraph_chunks}")
+    print(f"\nSummary:")
+    print(f"  Chunks split by level 1 headings (#): {count_level_1_chunks}")
+    print(f"  Chunks split by grouped level 2 headings (##): {count_level_2_chunks}")
+    print(f"  Chunks split by paragraphs: {count_paragraph_chunks}")
 
     return batches
+
+

clean.py

@@ -104,8 +104,10 @@ def process_content(content: str) -> str:
     return content_cleaned
 
 if __name__ == "__main__":
+    from pprint import pprint
     input_file = "content.txt"
     from chunking import split_content_into_batches
+    from embedding_gen import embed_text_batches_azure
     # Read content from file
     with open(input_file, "r", encoding="utf-8") as f:
         original_content = f.read()
@@ -113,16 +115,18 @@ if __name__ == "__main__":
     # Convert HTML tables to markdown tables
     converted_content = process_content(original_content)
     content=split_content_into_batches(converted_content)
-    print(len(content))
+    # print(content)
+    # print(title)
+
     # Print the converted content (or you could write it to a file if needed)
-    from openai import AzureOpenAI
-    from config import azure_open_ai_key, azure_open_ai_url
-    from embedding_gen import embed_text_batches_azure
-    client = AzureOpenAI(
-    api_key=azure_open_ai_key,
-    azure_endpoint=azure_open_ai_url,
-    api_version="2023-05-15"
-)   
+#     from openai import AzureOpenAI
+#     from config import azure_open_ai_key, azure_open_ai_url
+#     from embedding_gen import embed_text_batches_azure
+#     client = AzureOpenAI(
+#     api_key=azure_open_ai_key,
+#     azure_endpoint=azure_open_ai_url,
+#     api_version="2023-05-15"
+# )   
     import time
     from qdrant_setup import batch_upsert_to_qdrant
     t1=time.time()

doc_anal.py

@@ -115,7 +115,7 @@ if __name__ == "__main__":
     import time
     t1=time.time()
     analysis_results = client.analyze(
-        source="https://hackrx.blob.core.windows.net/assets/policy.pdf?sv=2023-01-03&st=2025-07-04T09%3A11%3A24Z&se=2027-07-05T09%3A11%3A00Z&sr=b&sp=r&sig=N4a9OU0w0QXO6AOIBiu4bpl7AXvEZogeT%2FjUHNO7HzQ%3D"
+        source="https://hackrx.blob.core.windows.net/assets/Arogya%20Sanjeevani%20Policy%20-%20CIN%20-%20U10200WB1906GOI001713%201.pdf?sv=2023-01-03&st=2025-07-21T08%3A29%3A02Z&se=2025-09-22T08%3A29%3A00Z&sr=b&sp=r&sig=nzrz1K9Iurt%2BBXom%2FB%2BMPTFMFP3PRnIvEsipAX10Ig4%3D"
     )
     t2=time.time()
     print("analysis_time",t2-t1)
@@ -139,7 +139,7 @@ if __name__ == "__main__":
 
     # Write the content text to a file
     content = analyze_result.get("content", "")
-    with open("content.txt", "w", encoding="utf-8") as f_content:
+    with open("content2.txt", "w", encoding="utf-8") as f_content:
         f_content.write(content)
 
     # Write a JSON dump of the tables extracted (if any)

uv.lock

@@ -32,6 +32,7 @@ dependencies = [
     { name = "bs4" },
     { name = "dotenv" },
     { name = "fastapi" },
+    { name = "langchain" },
     { name = "openai" },
     { name = "pydantic" },
     { name = "qdrant-client" },
@@ -44,6 +45,7 @@ requires-dist = [
     { name = "bs4", specifier = ">=0.0.2" },
     { name = "dotenv", specifier = ">=0.9.9" },
     { name = "fastapi", specifier = ">=0.116.1" },
+    { name = "langchain", specifier = ">=0.3.27" },
     { name = "openai", specifier = ">=1.97.1" },
     { name = "pydantic", specifier = ">=2.11.7" },
     { name = "qdrant-client", specifier = ">=1.15.0" },
@@ -85,6 +87,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
 ]
 
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621, upload-time = "2024-09-04T20:45:21.852Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989, upload-time = "2024-09-04T20:44:28.956Z" },
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+    { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792, upload-time = "2024-09-04T20:44:32.01Z" },
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