Metadata filtering

main.py

@@ -2,7 +2,6 @@ import datetime
 import logging
 import os
 import sqlite3
-from typing import Any, Union
 
 import argparse
 import chromadb
@@ -13,11 +12,12 @@ from request import PaperlessNGXService
 from chunker import Chunker
 from cleaner import pdf_to_image, summarize_pdf_image
 from llm import LLMClient
+from query import QueryGenerator
 
 
 from dotenv import load_dotenv
 
-load_dotenv()
+_dotenv_loaded = load_dotenv()
 
 # Configure ollama client with URL from environment or default to localhost
 ollama_client = ollama.Client(
@@ -43,18 +43,18 @@ ppngx = PaperlessNGXService()
 llm_client = LLMClient()
 
 
-def index_using_pdf_llm():
+def index_using_pdf_llm(doctypes):
     logging.info("reindex data...")
     files = ppngx.get_data()
     for file in files:
-        document_id = file["id"]
+        document_id: int = file["id"]
         pdf_path = ppngx.download_pdf_from_id(id=document_id)
         image_paths = pdf_to_image(filepath=pdf_path)
         print(f"summarizing {file}")
         generated_summary = summarize_pdf_image(filepaths=image_paths)
         file["content"] = generated_summary
 
-    chunk_data(files, simba_docs)
+    chunk_data(files, simba_docs, doctypes=doctypes)
 
 
 def date_to_epoch(date_str: str) -> float:
@@ -71,7 +71,7 @@ def date_to_epoch(date_str: str) -> float:
     return date.timestamp()
 
 
-def chunk_data(docs: list[dict[str, Union[str, Any]]], collection, doctypes):
+def chunk_data(docs, collection, doctypes):
     # Step 2: Create chunks
     chunker = Chunker(collection)
 
@@ -121,13 +121,15 @@ def consult_oracle(input: str, collection):
     start_time = time.time()
 
     # Ask
-    # print("Starting query generation")
+    print("Starting query generation")
-    # qg_start = time.time()
+    qg_start = time.time()
-    # qg = QueryGenerator()
+    qg = QueryGenerator()
+    doctype_query = qg.get_doctype_query(input=input)
     # metadata_filter = qg.get_query(input)
-    # qg_end = time.time()
+    metadata_filter = {**doctype_query}
-    # print(f"Query generation took {qg_end - qg_start:.2f} seconds")
+    print(metadata_filter)
-    # print(metadata_filter)
+    qg_end = time.time()
+    print(f"Query generation took {qg_end - qg_start:.2f} seconds")
 
     print("Starting embedding generation")
     embedding_start = time.time()
@@ -140,8 +142,9 @@ def consult_oracle(input: str, collection):
     results = collection.query(
         query_texts=[input],
         query_embeddings=embeddings,
-        # where=metadata_filter,
+        where=metadata_filter,
     )
+    print(results)
     query_end = time.time()
     print(f"Collection query took {query_end - query_start:.2f} seconds")
 
@@ -193,18 +196,28 @@ def filter_indexed_files(docs):
 if __name__ == "__main__":
     args = parser.parse_args()
     if args.reindex:
+        with sqlite3.connect("./visited.db") as conn:
+            c = conn.cursor()
+            c.execute("DELETE FROM indexed_documents")
+
         print("Fetching documents from Paperless-NGX")
         ppngx = PaperlessNGXService()
         docs = ppngx.get_data()
         docs = filter_indexed_files(docs)
         print(f"Fetched {len(docs)} documents")
-        #
+
+        # Delete all chromadb data
+        ids = simba_docs.get(ids=None, limit=None, offset=0)
+        all_ids = ids["ids"]
+        if len(all_ids) > 0:
+            simba_docs.delete(ids=all_ids)
+
+        # Chunk documents
         print("Chunking documents now ...")
         tag_lookup = ppngx.get_tags()
         doctype_lookup = ppngx.get_doctypes()
         chunk_data(docs, collection=simba_docs, doctypes=doctype_lookup)
         print("Done chunking documents")
-        # index_using_pdf_llm()
 
     # if args.index:
     # with open(args.index) as file:

query.py

@@ -9,7 +9,9 @@ from openai import OpenAI
 from pydantic import BaseModel, Field
 
 # Configure ollama client with URL from environment or default to localhost
-ollama_client = Client(host=os.getenv("OLLAMA_URL", "http://localhost:11434"))
+ollama_client = Client(
+    host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
+)
 
 # This uses inferred filters — which means using LLM to create the metadata filters
 
@@ -53,8 +55,8 @@ class DocumentType(BaseModel):
 
 
 PROMPT = """
-You are an information specialist that processes user queries. The current year is 2025. The user queries are all about 
+You are an information specialist that processes user queries. The current year is 2025. The user queries are all about
-a cat, Simba, and its records. The types of records are listed below. Using the query, extract the 
+a cat, Simba, and its records. The types of records are listed below. Using the query, extract the
 the date range the user is trying to query. You should return it as a JSON. The date tag is created_date. Return the date in epoch time.
 
 If the created_date cannot be ascertained, set it to epoch time start.
@@ -97,7 +99,17 @@ Only return the extracted metadata fields. Make sure the extracted metadata fiel
 """
 
 
-DOCTYPE_PROMPT = f"You are an information specialist that processes user queries. A query can have two tags attached from the following options. Based on the query, determine which of the following options is most appropriate: {','.join(DOCTYPE_OPTIONS)}"
+DOCTYPE_PROMPT = f"""You are an information specialist that processes user queries. A query can have two tags attached from the following options. Based on the query, determine which of the following options is most appropriate: {",".join(DOCTYPE_OPTIONS)}
+
+### Example 1
+Query: "Who is Simba's current vet?"
+Tags: ["Bill", "Medical Record"]
+
+
+### Example 2
+Query: "Who does Simba know?"
+Tags: ["Letter", "Documentation"]
+"""
 
 
 class QueryGenerator:
@@ -118,7 +130,6 @@ class QueryGenerator:
         return date.timestamp()
 
     def get_doctype_query(self, input: str):
-        print(DOCTYPE_PROMPT)
         client = OpenAI()
         response = client.chat.completions.create(
             messages=[
@@ -140,8 +151,8 @@ class QueryGenerator:
 
         response_json_str = response.choices[0].message.content
         type_data = json.loads(response_json_str)
-        print(type_data)
+        metadata_query = {"document_type": {"$in": type_data["type"]}}
-        return type_data
+        return metadata_query
 
     def get_query(self, input: str):
         client = OpenAI()