reverted back to pre-parallel version

RAG.py

@@ -12,10 +12,10 @@
 from langchain_community.retrievers import BM25Retriever
 import requests
 from typing import Dict, Any, Optional, List, Tuple
+import json
 import logging
-import concurrent.futures
 
-def retrieve(query: str,vectorstore:PineconeVectorStore, k: int = 100) -> Tuple[List[Document], List[float]]:    
+def retrieve(query: str,vectorstore:PineconeVectorStore, k: int = 1000) -> Tuple[List[Document], List[float]]:    
     start = time.time()
     # pinecone_api_key = os.getenv("PINECONE_API_KEY")
     # pc = Pinecone(api_key=pinecone_api_key)
@@ -39,6 +39,7 @@ def retrieve(query: str,vectorstore:PineconeVectorStore, k: int = 100) -> Tuple[
 
 def safe_get_json(url: str) -> Optional[Dict]:
     """Safely fetch and parse JSON from a URL."""
+    print("Fetching JSON")
     try:
         response = requests.get(url, timeout=10)
         response.raise_for_status()
@@ -63,52 +64,26 @@ def extract_text_from_json(json_data: Dict) -> str:
 
     return " ".join(text_parts) if text_parts else "No content available"
 
-def process_single_document(doc: Document) -> Optional[Document]:
-    """Process a single document by fetching and extracting metadata."""
-    if not doc.metadata.get('source'):
-        return None
-
-    url = f"https://www.digitalcommonwealth.org/search/{doc.metadata['source']}"
-    json_data = safe_get_json(f"{url}.json")
-
-    if json_data:
-        text_content = extract_text_from_json(json_data)
-        if text_content:
-            return Document(
-                page_content=text_content, 
-                metadata={
-                    "source": doc.metadata['source'],
-                    "field": doc.metadata['field'],
-                    "URL": url
-                }
-            )
-    return None
-
-def rerank(documents: List[Document], query: str, max_workers: int = 1) -> List[Document]:
-    """Ingest more metadata and rerank documents using BM25 with parallel processing."""
+def rerank(documents: List[Document], query: str) -> List[Document]:
+    """Ingest more metadata. Rerank documents using BM25"""
     start = time.time()
     if not documents:
         return []
 
-    meta_start = time.time()
     full_docs = []
-
-    # Process documents in parallel using ThreadPoolExecutor
-    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-        # Submit all document processing tasks
-        future_to_doc = {
-            executor.submit(process_single_document, doc): doc 
-            for doc in documents
-        }
+    meta_start = time.time()
+    for doc in documents:
+        if not doc.metadata.get('source'):
+            continue
+
+        url = f"https://www.digitalcommonwealth.org/search/{doc.metadata['source']}"
+        json_data = safe_get_json(f"{url}.json")
 
-        # Collect results as they complete
-        for future in concurrent.futures.as_completed(future_to_doc):
-            processed_doc = future.result()
-            if processed_doc:extract_text_from_json():
-                full_docs.append(processed_doc)
-
+        if json_data:
+            text_content = extract_text_from_json(json_data)
+            if text_content:  # Only add documents with actual content
+                full_docs.append(Document(page_content=text_content, metadata={"source":doc.metadata['source'],"field":doc.metadata['field'],"URL":url}))
     logging.info(f"Took {time.time()-meta_start} seconds to retrieve all metadata")
-
     # If no valid documents were processed, return empty list
     if not full_docs:
         return []
@@ -117,7 +92,7 @@ def rerank(documents: List[Document], query: str, max_workers: int = 1) -> List[
     reranker = BM25Retriever.from_documents(full_docs, k=min(10, len(full_docs)))
     reranked_docs = reranker.invoke(query)
     logging.info(f"Finished reranking: {time.time()-start}")
-    return full_docs
+    return reranked_docs
 
 def parse_xml_and_query(query:str,xml_string:str) -> str:
     """parse xml and return rephrased query"""
@@ -203,7 +178,7 @@ def RAG(llm: Any, query: str,vectorstore:PineconeVectorStore, top: int = 10, k:
             First, reason about the answer between <REASONING></REASONING> headers,
             based on the context determine if there is sufficient material for answering the exact question,
             return either <VALID>YES</VALID> or <VALID>NO</VALID>
-            then return a response between <RESPONSE></RESPONSE> headers, your response should be well formatted and an individual summary of each piece of relevant context:
+            then return a response between <RESPONSE></RESPONSE> headers:
             Here is an example
             <EXAMPLE>
             <QUERY>Are pineapples a good fuel for cars?</QUERY>