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https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
Community[minor]: Added checksum in while send data to pebblo-cloud (#23968)
- **Description:** - Updated checksum in doc metadata - Sending checksum and removing actual content, while sending data to `pebblo-cloud` if `classifier-location `is `pebblo-cloud` in `/loader/doc` API - Adding `pb_id` i.e. pebblo id to doc metadata - Refactoring as needed. - Sending `content-checksum` and removing actual content, while sending data to `pebblo-cloud` if `classifier-location `is `pebblo-cloud` in `prmopt` API - **Issue:** NA - **Dependencies:** NA - **Tests:** Updated - **Docs** NA --------- Co-authored-by: dristy.cd <dristy@clouddefense.io>
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9aae8ef416
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@ -124,6 +124,11 @@ class PebbloRetrievalQA(Chain):
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),
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"doc": doc.page_content,
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"vector_db": self.retriever.vectorstore.__class__.__name__,
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**(
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{"pb_checksum": doc.metadata.get("pb_checksum")}
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if doc.metadata.get("pb_checksum")
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else {}
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),
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}
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for doc in docs
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if isinstance(doc, Document)
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@ -457,25 +462,24 @@ class PebbloRetrievalQA(Chain):
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if self.api_key:
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if self.classifier_location == "local":
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if pebblo_resp:
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payload["response"] = (
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json.loads(pebblo_resp.text)
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.get("retrieval_data", {})
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.get("response", {})
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resp = json.loads(pebblo_resp.text)
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if resp:
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payload["response"].update(
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resp.get("retrieval_data", {}).get("response", {})
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)
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payload["context"] = (
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json.loads(pebblo_resp.text)
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.get("retrieval_data", {})
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.get("context", [])
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)
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payload["prompt"] = (
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json.loads(pebblo_resp.text)
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.get("retrieval_data", {})
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.get("prompt", {})
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payload["response"].pop("data")
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payload["prompt"].update(
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resp.get("retrieval_data", {}).get("prompt", {})
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)
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payload["prompt"].pop("data")
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context = payload["context"]
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for context_data in context:
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context_data.pop("doc")
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payload["context"] = context
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else:
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payload["response"] = None
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payload["context"] = None
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payload["prompt"] = None
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payload["response"] = {}
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payload["prompt"] = {}
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payload["context"] = []
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headers.update({"x-api-key": self.api_key})
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pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{PROMPT_URL}"
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try:
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@ -129,6 +129,7 @@ class Context(BaseModel):
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retrieved_from: Optional[str]
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doc: Optional[str]
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vector_db: str
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pb_checksum: Optional[str]
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class Prompt(BaseModel):
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@ -5,7 +5,7 @@ import logging
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import os
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import uuid
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from http import HTTPStatus
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from typing import Any, Dict, Iterator, List, Optional, Union
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from typing import Any, Dict, Iterator, List, Optional
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import requests # type: ignore
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from langchain_core.documents import Document
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@ -61,7 +61,7 @@ class PebbloSafeLoader(BaseLoader):
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self.source_path = get_loader_full_path(self.loader)
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self.source_owner = PebbloSafeLoader.get_file_owner_from_path(self.source_path)
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self.docs: List[Document] = []
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self.docs_with_id: Union[List[IndexedDocument], List[Document], List] = []
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self.docs_with_id: List[IndexedDocument] = []
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loader_name = str(type(self.loader)).split(".")[-1].split("'")[0]
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self.source_type = get_loader_type(loader_name)
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self.source_path_size = self.get_source_size(self.source_path)
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@ -89,17 +89,13 @@ class PebbloSafeLoader(BaseLoader):
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list: Documents fetched from load method of the wrapped `loader`.
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"""
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self.docs = self.loader.load()
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# Add pebblo-specific metadata to docs
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self._add_pebblo_specific_metadata()
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if not self.load_semantic:
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self._classify_doc(self.docs, loading_end=True)
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return self.docs
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self.docs_with_id = self._index_docs()
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classified_docs = self._classify_doc(self.docs_with_id, loading_end=True)
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self.docs_with_id = self._add_semantic_to_docs(
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self.docs_with_id, classified_docs
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)
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self.docs = self._unindex_docs(self.docs_with_id) # type: ignore
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classified_docs = self._classify_doc(loading_end=True)
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self._add_pebblo_specific_metadata(classified_docs)
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if self.load_semantic:
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self.docs = self._add_semantic_to_docs(classified_docs)
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else:
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self.docs = self._unindex_docs() # type: ignore
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return self.docs
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def lazy_load(self) -> Iterator[Document]:
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@ -125,18 +121,13 @@ class PebbloSafeLoader(BaseLoader):
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self.docs = []
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break
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self.docs = list((doc,))
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# Add pebblo-specific metadata to docs
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self._add_pebblo_specific_metadata()
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if not self.load_semantic:
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self._classify_doc(self.docs, loading_end=True)
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yield self.docs[0]
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else:
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self.docs_with_id = self._index_docs()
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classified_doc = self._classify_doc(self.docs)
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self.docs_with_id = self._add_semantic_to_docs(
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self.docs_with_id, classified_doc
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)
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self.docs = self._unindex_docs(self.docs_with_id) # type: ignore
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classified_doc = self._classify_doc()
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self._add_pebblo_specific_metadata(classified_doc)
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if self.load_semantic:
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self.docs = self._add_semantic_to_docs(classified_doc)
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else:
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self.docs = self._unindex_docs()
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yield self.docs[0]
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@classmethod
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@ -147,13 +138,12 @@ class PebbloSafeLoader(BaseLoader):
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def set_loader_sent(cls) -> None:
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cls._loader_sent = True
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def _classify_doc(self, loaded_docs: list, loading_end: bool = False) -> list:
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def _classify_doc(self, loading_end: bool = False) -> dict:
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"""Send documents fetched from loader to pebblo-server. Then send
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classified documents to Daxa cloud(If api_key is present). Internal method.
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Args:
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loaded_docs (list): List of documents fetched from loader's load operation.
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loading_end (bool, optional): Flag indicating the halt of data
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loading by loader. Defaults to False.
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"""
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@ -163,9 +153,8 @@ class PebbloSafeLoader(BaseLoader):
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}
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if loading_end is True:
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PebbloSafeLoader.set_loader_sent()
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doc_content = [doc.dict() for doc in loaded_docs]
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doc_content = [doc.dict() for doc in self.docs_with_id]
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docs = []
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classified_docs = []
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for doc in doc_content:
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doc_metadata = doc.get("metadata", {})
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doc_authorized_identities = doc_metadata.get("authorized_identities", [])
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@ -183,12 +172,12 @@ class PebbloSafeLoader(BaseLoader):
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page_content = str(doc.get("page_content"))
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page_content_size = self.calculate_content_size(page_content)
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self.source_aggregate_size += page_content_size
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doc_id = doc.get("id", None) or 0
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doc_id = doc.get("pb_id", None) or 0
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docs.append(
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{
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"doc": page_content,
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"source_path": doc_source_path,
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"id": doc_id,
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"pb_id": doc_id,
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"last_modified": doc.get("metadata", {}).get("last_modified"),
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"file_owner": doc_source_owner,
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**(
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@ -221,6 +210,7 @@ class PebbloSafeLoader(BaseLoader):
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self.source_aggregate_size
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)
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payload = Doc(**payload).dict(exclude_unset=True)
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classified_docs = {}
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# Raw payload to be sent to classifier
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if self.classifier_location == "local":
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load_doc_url = f"{self.classifier_url}{LOADER_DOC_URL}"
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@ -228,7 +218,10 @@ class PebbloSafeLoader(BaseLoader):
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pebblo_resp = requests.post(
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load_doc_url, headers=headers, json=payload, timeout=300
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)
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classified_docs = json.loads(pebblo_resp.text).get("docs", None)
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# Updating the structure of pebblo response docs for efficient searching
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for classified_doc in json.loads(pebblo_resp.text).get("docs", []):
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classified_docs.update({classified_doc["pb_id"]: classified_doc})
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if pebblo_resp.status_code not in [
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HTTPStatus.OK,
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HTTPStatus.BAD_GATEWAY,
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@ -257,7 +250,21 @@ class PebbloSafeLoader(BaseLoader):
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if self.api_key:
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if self.classifier_location == "local":
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payload["docs"] = classified_docs
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docs = payload["docs"]
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for doc_data in docs:
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classified_data = classified_docs.get(doc_data["pb_id"], {})
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doc_data.update(
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{
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"pb_checksum": classified_data.get("pb_checksum", None),
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"loader_source_path": classified_data.get(
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"loader_source_path", None
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),
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"entities": classified_data.get("entities", {}),
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"topics": classified_data.get("topics", {}),
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}
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)
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doc_data.pop("doc")
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headers.update({"x-api-key": self.api_key})
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pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{LOADER_DOC_URL}"
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try:
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@ -453,33 +460,29 @@ class PebbloSafeLoader(BaseLoader):
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List[IndexedDocument]: A list of IndexedDocument objects with unique IDs.
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"""
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docs_with_id = [
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IndexedDocument(id=hex(i)[2:], **doc.dict())
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IndexedDocument(pb_id=str(i), **doc.dict())
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for i, doc in enumerate(self.docs)
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]
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return docs_with_id
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def _add_semantic_to_docs(
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self, docs_with_id: List[IndexedDocument], classified_docs: List[dict]
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) -> List[Document]:
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def _add_semantic_to_docs(self, classified_docs: Dict) -> List[Document]:
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"""
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Adds semantic metadata to the given list of documents.
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Args:
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docs_with_id (List[IndexedDocument]): A list of IndexedDocument objects
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containing the documents with their IDs.
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classified_docs (List[dict]): A list of dictionaries containing the
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classified documents.
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classified_docs (Dict): A dictionary of dictionaries containing the
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classified documents with pb_id as key.
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Returns:
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List[Document]: A list of Document objects with added semantic metadata.
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"""
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indexed_docs = {
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doc.id: Document(page_content=doc.page_content, metadata=doc.metadata)
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for doc in docs_with_id
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doc.pb_id: Document(page_content=doc.page_content, metadata=doc.metadata)
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for doc in self.docs_with_id
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}
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for classified_doc in classified_docs:
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doc_id = classified_doc.get("id")
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for classified_doc in classified_docs.values():
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doc_id = classified_doc.get("pb_id")
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if doc_id in indexed_docs:
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self._add_semantic_to_doc(indexed_docs[doc_id], classified_doc)
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@ -487,19 +490,16 @@ class PebbloSafeLoader(BaseLoader):
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return semantic_metadata_docs
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def _unindex_docs(self, docs_with_id: List[IndexedDocument]) -> List[Document]:
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def _unindex_docs(self) -> List[Document]:
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"""
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Converts a list of IndexedDocument objects to a list of Document objects.
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Args:
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docs_with_id (List[IndexedDocument]): A list of IndexedDocument objects.
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Returns:
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List[Document]: A list of Document objects.
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"""
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docs = [
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Document(page_content=doc.page_content, metadata=doc.metadata)
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for i, doc in enumerate(docs_with_id)
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for i, doc in enumerate(self.docs_with_id)
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]
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return docs
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@ -522,12 +522,16 @@ class PebbloSafeLoader(BaseLoader):
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)
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return doc
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def _add_pebblo_specific_metadata(self) -> None:
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def _add_pebblo_specific_metadata(self, classified_docs: dict) -> None:
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"""Add Pebblo specific metadata to documents."""
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for doc in self.docs:
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for doc in self.docs_with_id:
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doc_metadata = doc.metadata
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doc_metadata["full_path"] = get_full_path(
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doc_metadata.get(
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"full_path", doc_metadata.get("source", self.source_path)
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)
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)
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doc_metadata["pb_id"] = doc.pb_id
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doc_metadata["pb_checksum"] = classified_docs.get(doc.pb_id, {}).get(
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"pb_checksum", None
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)
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@ -66,7 +66,7 @@ logger = logging.getLogger(__name__)
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class IndexedDocument(Document):
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"""Pebblo Indexed Document."""
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id: str
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pb_id: str
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"""Unique ID of the document."""
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@ -65,12 +65,26 @@ def test_csv_loader_load_valid_data(mocker: MockerFixture) -> None:
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full_file_path = os.path.abspath(file_path)
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expected_docs = [
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Document(
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metadata={
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"source": full_file_path,
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"row": 0,
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"full_path": full_file_path,
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"pb_id": "0",
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# For UT as here we are not calculating checksum
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"pb_checksum": None,
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},
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page_content="column1: value1\ncolumn2: value2\ncolumn3: value3",
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metadata={"source": file_path, "row": 0, "full_path": full_file_path},
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),
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Document(
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metadata={
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"source": full_file_path,
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"row": 1,
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"full_path": full_file_path,
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"pb_id": "1",
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# For UT as here we are not calculating checksum
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"pb_checksum": None,
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},
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page_content="column1: value4\ncolumn2: value5\ncolumn3: value6",
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metadata={"source": file_path, "row": 1, "full_path": full_file_path},
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),
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]
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