Feat/302 telegram raw vectorize

dags/hivemind_etl_helpers/ingestion_pipeline.py

@@ -1,5 +1,7 @@
 import logging
+from datetime import datetime
 
+from dateutil.parser import parse
 from hivemind_etl_helpers.src.utils.credentials import load_redis_credentials
 from hivemind_etl_helpers.src.utils.mongo import get_mongo_uri
 from hivemind_etl_helpers.src.utils.redis import RedisSingleton
@@ -10,8 +12,11 @@
     IngestionPipeline,
 )
 from llama_index.core.node_parser import SemanticSplitterNodeParser
+from llama_index.core.schema import BaseNode
 from llama_index.storage.docstore.mongodb import MongoDocumentStore
 from llama_index.storage.kvstore.redis import RedisKVStore as RedisCache
+from qdrant_client.conversions import common_types as qdrant_types
+from qdrant_client.http import models
 from tc_hivemind_backend.db.credentials import load_postgres_credentials
 from tc_hivemind_backend.db.qdrant import QdrantSingleton
 from tc_hivemind_backend.db.utils.model_hyperparams import load_model_hyperparams
@@ -37,7 +42,23 @@ def __init__(self, community_id: str, collection_name: str, testing: bool = Fals
         )
         self.redis_client = RedisSingleton.get_instance().get_client()
 
-    def run_pipeline(self, docs: list[Document]):
+    def run_pipeline(self, docs: list[Document]) -> list[BaseNode]:
+        """
+        vectorize and ingest data into a qdrant collection
+
+        Note: This will handle duplicate documents by doing an upsert operation.
+
+        Parameters
+        ------------
+        docs : list[llama_index.Document]
+            list of llama-index documents
+
+        Returns
+        ---------
+        nodes : list[BaseNode]
+            The set of transformed and loaded Nodes/Documents
+            (transformation is chunking and embedding of data)
+        """
         # qdrant is just collection based and doesn't have any database
         logging.info(
             f"{len(docs)} docuemnts was extracted and now loading into QDrant DB!"
@@ -68,3 +89,83 @@ def run_pipeline(self, docs: list[Document]):
 
         nodes = pipeline.run(documents=docs, show_progress=True)
         return nodes
+
+    def _create_payload_index(
+        self,
+        field_name: str,
+        field_schema: qdrant_types.PayloadSchemaType,
+    ) -> qdrant_types.UpdateResult:
+        """
+        Creates an index on a field under the payload of points in qdrant db
+
+        Note: this could be used for payload fields that we want to scroll for after
+
+        Parameters
+        ------------
+        field_name : str
+            the field name under points' payload to create the index for
+        field_schema : qdrant_client.conversions.common_types.PayloadSchemaType
+            the schema type of the field
+
+        Returns
+        -----------
+        operation_result : qdrant_client.conversions.common_types.UpdateResult
+            the payload index creation type
+        """
+        operation_result = self.qdrant_client.create_payload_index(
+            collection_name=self.collection_name,
+            field_name=field_name,
+            field_schema=field_schema,
+        )
+
+        return operation_result
+
+    def get_latest_document_date(self, field_name: str) -> datetime | None:
+        """
+        get the latest date for the most recent available document
+
+        NOTE: the given `field_name` under the points' schema MUST CONTAIN A VALUE HAVING DATE FORMAT (or a string format). If not, errors might raise in result of this function
+
+        Parameters
+        ------------
+        field_name : str
+            the datetime field name in qdrant points' payload
+
+        Returns
+        ---------
+        latest_date : datetime.datetime | None
+            the datetime for the document containing the latest date
+            if no document or any errors raised, we would return `None`
+        """
+        latest_date: datetime | None = None
+        try:
+            result = self._create_payload_index(
+                field_name=field_name,
+                field_schema=models.PayloadSchemaType.DATETIME,
+            )
+            if result.status.name == "COMPLETED":
+                latest_document = self.qdrant_client.scroll(
+                    collection_name=self.collection_name,
+                    limit=1,
+                    with_payload=True,
+                    order_by=models.OrderBy(
+                        key=field_name,
+                        direction=models.Direction.DESC,
+                    ),
+                )
+
+                if not latest_document[0]:
+                    logging.info("No documents found in the collection.")
+                    latest_date = None
+                else:
+                    latest_date = parse(latest_document[0][0].payload[field_name])
+
+            else:
+                raise ValueError(
+                    f"Index not created successfully! index creation result: {result}"
+                )
+        except Exception as exp:
+            logging.error(f"Error: {exp} while loading latest point!")
+            latest_date = None
+
+        return latest_date

dags/hivemind_etl_helpers/src/db/telegram/extract/__init__.py

@@ -0,0 +1,2 @@
+from .messages import ExtractMessages
+from .tc_chats import TelegramChats

dags/hivemind_etl_helpers/src/db/telegram/extract/messages.py

@@ -0,0 +1,82 @@
+from datetime import datetime
+
+from hivemind_etl_helpers.src.db.telegram.schema import TelegramMessagesModel
+from tc_neo4j_lib import Neo4jOps
+
+
+class ExtractMessages:
+    def __init__(self, chat_id: str) -> None:
+        self.chat_id = chat_id
+        self._connection = Neo4jOps.get_instance()
+
+    def extract(self, from_date: datetime | None = None) -> list[TelegramMessagesModel]:
+        """
+        extract messages related to the given `chat_id`
+
+        Parameters
+        -----------
+        from_date : datetime | None
+            load from a specific date
+            if not given, load all data
+
+        Returns
+        ---------
+        tg_messages : list[TelegramMessagesModel]
+            the telegram messages
+        """
+        # initialize
+        where_clause: str | None = None
+        from_date_timestamp: int | None = None
+
+        if from_date:
+            from_date_timestamp = int(from_date.timestamp() * 1000)
+            where_clause = """
+            AND message.date >= $from_date_timestamp
+            """
+        query = f"""
+            MATCH (c:TGChat {{id: $chat_id}})<-[:SENT_IN]-(message:TGMessage)
+            WHERE message.text IS NOT NULL
+            {where_clause if where_clause else ""}
+            WITH
+                message.id AS message_id,
+                MAX(message.updated_at) AS latest_msg_time,
+                MIN(message.updated_at) AS first_msg_time
+
+            MATCH (first_message:TGMessage {{id: message_id, updated_at: first_msg_time}})
+            MATCH (last_edit:TGMessage {{id: message_id, updated_at: latest_msg_time}})
+
+            WITH
+                first_message AS message,
+                last_edit.updated_at AS edited_at,
+                last_edit.text AS message_text
+            OPTIONAL MATCH (author:TGUser)-[created_rel:CREATED_MESSAGE]->(message)
+            OPTIONAL MATCH (reacted_user:TGUser)-[react_rel:REACTED_TO]->(message)
+            OPTIONAL MATCH (reply_msg:TGMessage)-[:REPLIED]->(message)
+            OPTIONAL MATCH (replied_user:TGUser)-[:CREATED_MESSAGE]->(reply_msg)
+            OPTIONAL MATCH (message)-[:MENTIONED]->(mentioned_user:TGUser)
+            RETURN
+                message.id AS message_id,
+                message_text,
+                author.username AS author_username,
+                message.date AS message_created_at,
+                edited_at AS message_edited_at,
+                COLLECT(DISTINCT mentioned_user.username) AS mentions,
+                COLLECT(DISTINCT replied_user.username) AS repliers,
+                COLLECT(DISTINCT reacted_user.username) AS reactors
+            ORDER BY message_created_at DESC
+        """
+
+        parameters = {"chat_id": self.chat_id}
+        if from_date_timestamp:
+            parameters["from_date_timestamp"] = from_date_timestamp
+
+        tg_messages = []
+        with self._connection.neo4j_driver.session() as session:
+            result = session.run(
+                query,
+                parameters=parameters,
+            )
+            messages = result.data()
+            tg_messages = [TelegramMessagesModel(**message) for message in messages]
+
+        return tg_messages

dags/hivemind_etl_helpers/src/db/telegram/extract/tc_chats.py

@@ -0,0 +1,33 @@
+import logging
+
+from tc_neo4j_lib import Neo4jOps
+
+
+class TelegramChats:
+    def __init__(self) -> None:
+        self._connection = Neo4jOps.get_instance()
+
+    def extract_chats(self) -> list[tuple[str, str]]:
+        """
+        extract the chat id and chat names
+
+        Returns
+        ---------
+        chat_info : list[tuple[str, str]]
+            a list of Telegram chat id and chat name
+        """
+        driver = self._connection.neo4j_driver
+
+        chat_info: list[str] = []
+        try:
+            with driver.session() as session:
+                records = session.run(
+                    "MATCH (c:TGChat) RETURN c.id as chat_id, c.title as name"
+                )
+                chat_info = [
+                    (str(record["chat_id"]), str(record["name"])) for record in records
+                ]
+        except Exception as exp:
+            logging.error(f"Exception during extracting chat ids. exp: {exp}")
+
+        return chat_info

dags/hivemind_etl_helpers/src/db/telegram/schema.py

@@ -0,0 +1,16 @@
+from pydantic import BaseModel
+
+
+class TelegramMessagesModel(BaseModel):
+    """
+    Represents a Telegram message with its associated metadata.
+    """
+
+    message_id: int
+    message_text: str
+    author_username: str
+    message_created_at: float
+    message_edited_at: float
+    mentions: list[str]
+    repliers: list[str]
+    reactors: list[str]

dags/hivemind_etl_helpers/src/db/telegram/transform/__init__.py

@@ -0,0 +1 @@
+from .messages import TransformMessages

dags/hivemind_etl_helpers/src/db/telegram/transform/messages.py

@@ -0,0 +1,59 @@
+from hivemind_etl_helpers.src.db.telegram.schema import TelegramMessagesModel
+from llama_index.core import Document
+
+
+class TransformMessages:
+    def __init__(self, chat_id: str, chat_name: str) -> None:
+        self.chat_id = chat_id
+        self.chat_name = chat_name
+
+    def transform(self, messages: list[TelegramMessagesModel]) -> list[Document]:
+        """
+        transform the given telegram messages to llama-index documents
+
+        Parameters
+        ----------
+        messages : list[TelegramMessagesModel]
+            the extracted telegram messages
+
+        Returns
+        ---------
+        transformed_docs : list[llama_index.core.Document]
+            a list of llama-index documents to be embedded & loaded into db
+        """
+        transformed_docs: list[Document] = []
+
+        for message in messages:
+            document = Document(
+                text=message.message_text,
+                doc_id=message.message_id,
+                metadata={
+                    "author": message.author_username,
+                    "createdAt": message.message_created_at,
+                    "updatedAt": message.message_edited_at,
+                    "mentions": message.mentions,
+                    "replies": message.repliers,
+                    "reactors": message.reactors,
+                    "chat_name": self.chat_name,
+                },
+                excluded_embed_metadata_keys=[
+                    "author",
+                    "createdAt",
+                    "updatedAt",
+                    "mentions",
+                    "replies",
+                    "reactors",
+                    "chat_name",
+                ],
+                excluded_llm_metadata_keys=[
+                    "createdAt",
+                    "updatedAt",
+                    "mentions",
+                    "replies",
+                    "reactors",
+                    "chat_name",
+                ],
+            )
+            transformed_docs.append(document)
+
+        return transformed_docs

dags/hivemind_etl_helpers/src/db/telegram/utility.py

@@ -0,0 +1,6 @@
+from hivemind_etl_helpers.src.db.telegram.utils import TelegramPlatform
+
+
+class TelegramUtils(TelegramPlatform):
+    def __init__(self, chat_id: str, chat_name: str) -> None:
+        super().__init__(chat_id, chat_name)

dags/hivemind_etl_helpers/src/db/telegram/utils/__init__.py

@@ -0,0 +1 @@
+from .platform import TelegramPlatform