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closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | """Validate that either folder_id or document_ids is set, but not both."""
if values.get("folder_id") and (
values.get("document_ids") or values.get("file_ids")
):
raise ValueError(
"Cannot specify both folder_id and document_ids nor "
"folder_id and file_ids"
)
if (
not values.get("folder_id")
and not values.get("document_ids")
and not values.get("file_ids")
):
raise ValueError("Must specify either folder_id, document_ids, or file_ids")
file_types = values.get("file_types")
if file_types:
if values.get("document_ids") or values.get("file_ids"):
raise ValueError(
"file_types can only be given when folder_id is given,"
" (not when document_ids or file_ids are given)."
)
type_mapping = {
"document": "application/vnd.google-apps.document",
"sheet": "application/vnd.google-apps.spreadsheet",
"pdf": "application/pdf",
} |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | allowed_types = list(type_mapping.keys()) + list(type_mapping.values())
short_names = ", ".join([f"'{x}'" for x in type_mapping.keys()])
full_names = ", ".join([f"'{x}'" for x in type_mapping.values()])
for file_type in file_types:
if file_type not in allowed_types:
raise ValueError(
f"Given file type {file_type} is not supported. "
f"Supported values are: {short_names}; and "
f"their full-form names: {full_names}"
)
def full_form(x: str) -> str:
return type_mapping[x] if x in type_mapping else x
values["file_types"] = [full_form(file_type) for file_type in file_types]
return values
@validator("credentials_path")
def validate_credentials_path(cls, v: Any, **kwargs: Any) -> Any:
"""Validate that credentials_path exists."""
if not v.exists():
raise ValueError(f"credentials_path {v} does not exist")
return v
def _load_credentials(self) -> Any:
"""Load credentials."""
try:
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
except ImportError: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | raise ImportError(
"You must run "
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib` "
"to use the Google Drive loader."
)
creds = None
if self.service_account_key.exists():
return service_account.Credentials.from_service_account_file(
str(self.service_account_key), scopes=SCOPES
)
if self.token_path.exists():
creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
str(self.credentials_path), SCOPES
)
creds = flow.run_local_server(port=0)
with open(self.token_path, "w") as token:
token.write(creds.to_json())
return creds
def _load_sheet_from_id(self, id: str) -> List[Document]:
"""Load a sheet and all tabs from an ID."""
from googleapiclient.discovery import build
creds = self._load_credentials()
sheets_service = build("sheets", "v4", credentials=creds) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | spreadsheet = sheets_service.spreadsheets().get(spreadsheetId=id).execute()
sheets = spreadsheet.get("sheets", [])
documents = []
for sheet in sheets:
sheet_name = sheet["properties"]["title"]
result = (
sheets_service.spreadsheets()
.values()
.get(spreadsheetId=id, range=sheet_name)
.execute()
)
values = result.get("values", [])
header = values[0]
for i, row in enumerate(values[1:], start=1):
metadata = {
"source": (
f"https://docs.google.com/spreadsheets/d/{id}/"
f"edit?gid={sheet['properties']['sheetId']}"
),
"title": f"{spreadsheet['properties']['title']} - {sheet_name}",
"row": i,
}
content = []
for j, v in enumerate(row):
title = header[j].strip() if len(header) > j else ""
content.append(f"{title}: {v.strip()}")
page_content = "\n".join(content)
documents.append(Document(page_content=page_content, metadata=metadata))
return documents
def _load_document_from_id(self, id: str) -> Document: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | """Load a document from an ID."""
from io import BytesIO
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from googleapiclient.http import MediaIoBaseDownload
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
file = service.files().get(fileId=id, supportsAllDrives=True).execute()
request = service.files().export_media(fileId=id, mimeType="text/plain")
fh = BytesIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
try:
while done is False:
status, done = downloader.next_chunk()
except HttpError as e:
if e.resp.status == 404:
print("File not found: {}".format(id))
else:
print("An error occurred: {}".format(e))
text = fh.getvalue().decode("utf-8")
metadata = {
"source": f"https://docs.google.com/document/d/{id}/edit",
"title": f"{file.get('name')}",
}
return Document(page_content=text, metadata=metadata)
def _load_documents_from_folder( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | self, folder_id: str, *, file_types: Optional[Sequence[str]] = None
) -> List[Document]:
"""Load documents from a folder."""
from googleapiclient.discovery import build
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
files = self._fetch_files_recursive(service, folder_id)
if file_types:
_files = [f for f in files if f["mimeType"] in file_types]
else:
_files = files
returns = []
for file in _files:
if file["mimeType"] == "application/vnd.google-apps.document":
returns.append(self._load_document_from_id(file["id"]))
elif file["mimeType"] == "application/vnd.google-apps.spreadsheet":
returns.extend(self._load_sheet_from_id(file["id"]))
elif file["mimeType"] == "application/pdf":
returns.extend(self._load_file_from_id(file["id"]))
else:
pass
return returns
def _fetch_files_recursive( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | self, service: Any, folder_id: str
) -> List[Dict[str, Union[str, List[str]]]]:
"""Fetch all files and subfolders recursively."""
results = (
service.files()
.list(
q=f"'{folder_id}' in parents",
pageSize=1000,
includeItemsFromAllDrives=True,
supportsAllDrives=True,
fields="nextPageToken, files(id, name, mimeType, parents)",
)
.execute()
)
files = results.get("files", [])
returns = []
for file in files:
if file["mimeType"] == "application/vnd.google-apps.folder":
if self.recursive:
returns.extend(self._fetch_files_recursive(service, file["id"]))
else:
returns.append(file)
return returns
def _load_documents_from_ids(self) -> List[Document]:
"""Load documents from a list of IDs."""
if not self.document_ids:
raise ValueError("document_ids must be set")
return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
def _load_file_from_id(self, id: str) -> List[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | """Load a file from an ID."""
from io import BytesIO
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
file = service.files().get(fileId=id, supportsAllDrives=True).execute()
request = service.files().get_media(fileId=id)
fh = BytesIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
while done is False:
status, done = downloader.next_chunk()
content = fh.getvalue()
from PyPDF2 import PdfReader
pdf_reader = PdfReader(BytesIO(content))
return [
Document(
page_content=page.extract_text(),
metadata={
"source": f"https://drive.google.com/file/d/{id}/view",
"title": f"{file.get('name')}",
"page": i,
},
)
for i, page in enumerate(pdf_reader.pages)
]
def _load_file_from_ids(self) -> List[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | "2023-05-22T21:21:14Z" | python | "2023-05-25T05:26:17Z" | langchain/document_loaders/googledrive.py | """Load files from a list of IDs."""
if not self.file_ids:
raise ValueError("file_ids must be set")
docs = []
for file_id in self.file_ids:
docs.extend(self._load_file_from_id(file_id))
return docs
def load(self) -> List[Document]:
"""Load documents."""
if self.folder_id:
return self._load_documents_from_folder(
self.folder_id, file_types=self.file_types
)
elif self.document_ids:
return self._load_documents_from_ids()
else:
return self._load_file_from_ids() |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Args:
no_avx2: Load FAISS strictly with no AVX2 optimization
so that the vectorstore is portable and compatible with other devices.
"""
if no_avx2 is None and "FAISS_NO_AVX2" in os.environ:
no_avx2 = bool(os.getenv("FAISS_NO_AVX2"))
try:
if no_avx2:
from faiss import swigfaiss as faiss
else:
import faiss
except ImportError:
raise ValueError(
"Could not import faiss python package. "
"Please install it with `pip install faiss` "
"or `pip install faiss-cpu` (depending on Python version)."
)
return faiss
def _default_relevance_score_fn(score: float) -> float: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Return a similarity score on a scale [0, 1]."""
return 1.0 - score / math.sqrt(2)
class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` python package installed.
Example:
.. code-block:: python
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
"""
def __init__( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
embedding_function: Callable,
index: Any,
docstore: Docstore,
index_to_docstore_id: Dict[int, str],
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score_fn,
normalize_L2: bool = False,
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
self.relevance_score_fn = relevance_score_fn
self._normalize_L2 = normalize_L2
def __add( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
starting_len = len(self.index_to_docstore_id)
faiss = dependable_faiss_import()
vector = np.array(embeddings, dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
self.index.add(vector)
full_info = [ |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | (starting_len + i, str(uuid.uuid4()), doc)
for i, doc in enumerate(documents)
]
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
return [_id for _, _id, _ in full_info]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
embeddings = [self.embedding_function(text) for text in texts]
return self.__add(texts, embeddings, metadatas, **kwargs)
def add_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
texts = [te[0] for te in text_embeddings]
embeddings = [te[1] for te in text_embeddings]
return self.__add(texts, embeddings, metadatas, **kwargs)
def similarity_search_with_score_by_vector( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, embedding: List[float], k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
faiss = dependable_faiss_import()
vector = np.array([embedding], dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
scores, indices = self.index.search(vector, k)
docs = []
for j, i in enumerate(indices[0]):
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k)
return docs
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
return [doc for doc, _ in docs_and_scores]
def similarity_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
_, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | docs.append(doc)
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
def merge_from(self, target: FAISS) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("Cannot merge with this type of docstore")
starting_len = len(self.index_to_docstore_id)
self.index.merge_from(target.index)
full_info = []
for i in target.index_to_docstore_id:
doc = target.docstore.search(target.index_to_docstore_id[i])
if not isinstance(doc, Document):
raise ValueError("Document should be returned")
full_info.append((starting_len + i, str(uuid.uuid4()), doc))
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
@classmethod
def __from(
cls,
texts: List[str], |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
normalize_L2: bool = False,
**kwargs: Any,
) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(
embedding.embed_query,
index,
docstore,
index_to_id,
normalize_L2=normalize_L2,
**kwargs,
)
@classmethod
def from_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
@classmethod
def from_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings)) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
def save_local(self, folder_path: str, index_name: str = "index") -> None:
"""Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "wb") as f:
pickle.dump((self.docstore, self.index_to_docstore_id), f)
@classmethod
def load_local( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls, folder_path: str, embeddings: Embeddings, index_name: str = "index"
) -> FAISS:
"""Load FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
faiss = dependable_faiss_import()
index = faiss.read_index(
str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevance_scores( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-21T16:39:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
" FAISS constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Args:
no_avx2: Load FAISS strictly with no AVX2 optimization
so that the vectorstore is portable and compatible with other devices.
"""
if no_avx2 is None and "FAISS_NO_AVX2" in os.environ:
no_avx2 = bool(os.getenv("FAISS_NO_AVX2"))
try:
if no_avx2:
from faiss import swigfaiss as faiss
else:
import faiss
except ImportError:
raise ValueError(
"Could not import faiss python package. "
"Please install it with `pip install faiss` "
"or `pip install faiss-cpu` (depending on Python version)."
)
return faiss
def _default_relevance_score_fn(score: float) -> float: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Return a similarity score on a scale [0, 1]."""
return 1.0 - score / math.sqrt(2)
class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` python package installed.
Example:
.. code-block:: python
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
"""
def __init__( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
embedding_function: Callable,
index: Any,
docstore: Docstore,
index_to_docstore_id: Dict[int, str],
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score_fn,
normalize_L2: bool = False,
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
self.relevance_score_fn = relevance_score_fn
self._normalize_L2 = normalize_L2
def __add( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
starting_len = len(self.index_to_docstore_id)
faiss = dependable_faiss_import()
vector = np.array(embeddings, dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
self.index.add(vector)
full_info = [ |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | (starting_len + i, str(uuid.uuid4()), doc)
for i, doc in enumerate(documents)
]
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
return [_id for _, _id, _ in full_info]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
embeddings = [self.embedding_function(text) for text in texts]
return self.__add(texts, embeddings, metadatas, **kwargs)
def add_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
texts = [te[0] for te in text_embeddings]
embeddings = [te[1] for te in text_embeddings]
return self.__add(texts, embeddings, metadatas, **kwargs)
def similarity_search_with_score_by_vector( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, embedding: List[float], k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
faiss = dependable_faiss_import()
vector = np.array([embedding], dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
scores, indices = self.index.search(vector, k)
docs = []
for j, i in enumerate(indices[0]):
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k)
return docs
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
return [doc for doc, _ in docs_and_scores]
def similarity_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
_, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | docs.append(doc)
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
def merge_from(self, target: FAISS) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("Cannot merge with this type of docstore")
starting_len = len(self.index_to_docstore_id)
self.index.merge_from(target.index)
full_info = []
for i in target.index_to_docstore_id:
doc = target.docstore.search(target.index_to_docstore_id[i])
if not isinstance(doc, Document):
raise ValueError("Document should be returned")
full_info.append((starting_len + i, str(uuid.uuid4()), doc))
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
@classmethod
def __from(
cls,
texts: List[str], |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
normalize_L2: bool = False,
**kwargs: Any,
) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(
embedding.embed_query,
index,
docstore,
index_to_id,
normalize_L2=normalize_L2,
**kwargs,
)
@classmethod
def from_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
@classmethod
def from_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings)) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
def save_local(self, folder_path: str, index_name: str = "index") -> None:
"""Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "wb") as f:
pickle.dump((self.docstore, self.index_to_docstore_id), f)
@classmethod
def load_local( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls, folder_path: str, embeddings: Embeddings, index_name: str = "index"
) -> FAISS:
"""Load FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
faiss = dependable_faiss_import()
index = faiss.read_index(
str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevance_scores( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-05-01T17:31:28Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
" FAISS constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Args:
no_avx2: Load FAISS strictly with no AVX2 optimization
so that the vectorstore is portable and compatible with other devices.
"""
if no_avx2 is None and "FAISS_NO_AVX2" in os.environ:
no_avx2 = bool(os.getenv("FAISS_NO_AVX2"))
try:
if no_avx2:
from faiss import swigfaiss as faiss
else:
import faiss
except ImportError:
raise ValueError(
"Could not import faiss python package. "
"Please install it with `pip install faiss` "
"or `pip install faiss-cpu` (depending on Python version)."
)
return faiss
def _default_relevance_score_fn(score: float) -> float: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Return a similarity score on a scale [0, 1]."""
return 1.0 - score / math.sqrt(2)
class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` python package installed.
Example:
.. code-block:: python
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
"""
def __init__( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
embedding_function: Callable,
index: Any,
docstore: Docstore,
index_to_docstore_id: Dict[int, str],
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score_fn,
normalize_L2: bool = False,
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
self.relevance_score_fn = relevance_score_fn
self._normalize_L2 = normalize_L2
def __add( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
starting_len = len(self.index_to_docstore_id)
faiss = dependable_faiss_import()
vector = np.array(embeddings, dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
self.index.add(vector)
full_info = [ |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | (starting_len + i, str(uuid.uuid4()), doc)
for i, doc in enumerate(documents)
]
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
return [_id for _, _id, _ in full_info]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
embeddings = [self.embedding_function(text) for text in texts]
return self.__add(texts, embeddings, metadatas, **kwargs)
def add_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"adding items, which {self.docstore} does not"
)
texts = [te[0] for te in text_embeddings]
embeddings = [te[1] for te in text_embeddings]
return self.__add(texts, embeddings, metadatas, **kwargs)
def similarity_search_with_score_by_vector( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, embedding: List[float], k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
faiss = dependable_faiss_import()
vector = np.array([embedding], dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
scores, indices = self.index.search(vector, k)
docs = []
for j, i in enumerate(indices[0]):
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
def similarity_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k)
return docs
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
return [doc for doc, _ in docs_and_scores]
def similarity_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
_, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | docs.append(doc)
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
def merge_from(self, target: FAISS) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | """Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError("Cannot merge with this type of docstore")
starting_len = len(self.index_to_docstore_id)
self.index.merge_from(target.index)
full_info = []
for i in target.index_to_docstore_id:
doc = target.docstore.search(target.index_to_docstore_id[i])
if not isinstance(doc, Document):
raise ValueError("Document should be returned")
full_info.append((starting_len + i, str(uuid.uuid4()), doc))
self.docstore.add({_id: doc for _, _id, doc in full_info})
index_to_id = {index: _id for index, _id, _ in full_info}
self.index_to_docstore_id.update(index_to_id)
@classmethod
def __from(
cls,
texts: List[str], |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
normalize_L2: bool = False,
**kwargs: Any,
) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(
embedding.embed_query,
index,
docstore,
index_to_id,
normalize_L2=normalize_L2,
**kwargs,
)
@classmethod
def from_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
@classmethod
def from_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings)) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts,
embeddings,
embedding,
metadatas,
**kwargs,
)
def save_local(self, folder_path: str, index_name: str = "index") -> None:
"""Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "wb") as f:
pickle.dump((self.docstore, self.index_to_docstore_id), f)
@classmethod
def load_local( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | cls, folder_path: str, embeddings: Embeddings, index_name: str = "index"
) -> FAISS:
"""Load FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
faiss = dependable_faiss_import()
index = faiss.read_index(
str(path / "{index_name}.faiss".format(index_name=index_name))
)
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevance_scores( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | "2023-04-11T06:33:19Z" | python | "2023-05-25T05:26:46Z" | langchain/vectorstores/faiss.py | self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
" FAISS constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/azure_openai.py | """Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class AzureChatOpenAI(ChatOpenAI): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/azure_openai.py | """Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use `deployment_name` in the
constructor to refer to the "Model deployment name" in the Azure portal.
In addition, you should have the ``openai`` python package installed, and the
following environment variables set or passed in constructor in lower case:
- ``OPENAI_API_TYPE`` (default: ``azure``)
- ``OPENAI_API_KEY``
- ``OPENAI_API_BASE``
- ``OPENAI_API_VERSION``
For exmaple, if you have `gpt-35-turbo` deployed, with the deployment name
`35-turbo-dev`, the constructor should look like:
.. code-block:: python
AzureChatOpenAI(
deployment_name="35-turbo-dev",
openai_api_version="2023-03-15-preview",
)
Be aware the API version may change.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
"""
deployment_name: str = ""
openai_api_type: str = "azure"
openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/azure_openai.py | """Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values,
"openai_api_key",
"OPENAI_API_KEY",
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
)
openai_api_version = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
)
openai_api_type = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION", |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/azure_openai.py | default="",
)
try:
import openai
openai.api_type = openai_api_type
openai.api_base = openai_api_base
openai.api_version = openai_api_version
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
return values
@property
def _default_params(self) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/azure_openai.py | """Get the default parameters for calling OpenAI API."""
return {
**super()._default_params,
"engine": self.deployment_name,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**self._default_params}
@property
def _llm_type(self) -> str:
return "azure-openai-chat"
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
for res in response["choices"]:
if res.get("finish_reason", None) == "content_filter":
raise ValueError(
"Azure has not provided the response due to a content"
" filter being triggered"
)
return super()._create_chat_result(response) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | )
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMessage,
ChatResult,
HumanMessage,
SystemMessage,
)
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
import tiktoken
logger = logging.getLogger(__name__)
def _import_tiktoken() -> Any:
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_token_ids. "
"Please install it with `pip install tiktoken`."
)
return tiktoken
def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | import openai
min_seconds = 1
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
def _convert_dict_to_message(_dict: dict) -> BaseMessage: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
return AIMessage(content=_dict["content"])
elif role == "system":
return SystemMessage(content=_dict["content"])
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
else:
raise ValueError(f"Got unknown type {message}")
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
class ChatOpenAI(BaseChatModel):
"""Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key. |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
"""
client: Any
model_name: str = Field(default="gpt-3.5-turbo", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
"""Base URL path for API requests,
leave blank if not using a proxy or service emulator."""
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """Configuration for this pydantic object."""
extra = Extra.ignore
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = cls.all_required_field_names()
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base", |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | "OPENAI_API_BASE",
default="",
)
try:
import openai
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
if openai_api_base:
openai.api_base = openai_api_base
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
return values
@property
def _default_params(self) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """Get the default parameters for calling OpenAI API."""
return {
"model": self.model_name,
"request_timeout": self.request_timeout,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**self.model_kwargs,
}
def _create_retry_decorator(self) -> Callable[[Any], Any]:
import openai
min_seconds = 1
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(self, **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs)
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
continue
token_usage = output["token_usage"]
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {"token_usage": overall_token_usage, "model_name": self.model_name}
def _generate( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
if self.streaming:
inner_completion = ""
role = "assistant"
params["stream"] = True
for stream_resp in self.completion_with_retry(
messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if run_manager:
run_manager.on_llm_new_token(token)
message = _convert_dict_to_message(
{"content": inner_completion, "role": role}
)
return ChatResult(generations=[ChatGeneration(message=message)])
response = self.completion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
def _create_message_dicts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
gen = ChatGeneration(message=message)
generations.append(gen)
llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
return ChatResult(generations=generations, llm_output=llm_output)
async def _agenerate( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
if self.streaming:
inner_completion = ""
role = "assistant"
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if run_manager:
await run_manager.on_llm_new_token(token)
message = _convert_dict_to_message(
{"content": inner_completion, "role": role}
)
return ChatResult(generations=[ChatGeneration(message=message)])
else:
response = await acompletion_with_retry(
self, messages=message_dicts, **params
)
return self._create_chat_result(response)
@property
def _identifying_params(self) -> Mapping[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | """Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | tiktoken_ = _import_tiktoken()
model = self.model_name
if model == "gpt-3.5-turbo":
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
model = "gpt-4-0314"
try:
encoding = tiktoken_.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken_.get_encoding(model)
return model, encoding
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
if sys.version_info[1] <= 7:
return super().get_token_ids(text)
_, encoding_model = self._get_encoding_model()
return encoding_model.encode(text)
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/chat_models/openai.py | if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model == "gpt-3.5-turbo-0301":
tokens_per_message = 4
tokens_per_name = -1
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}."
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
"information on how messages are converted to tokens."
)
num_tokens = 0
messages_dict = [_convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3
return num_tokens |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | """Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | import openai
min_seconds = 4
max_seconds = 10
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _embed_with_retry(**kwargs)
class OpenAIEmbeddings(BaseModel, Embeddings): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | """Wrapper around OpenAI embedding models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
from langchain.embeddings.openai import OpenAIEmbeddings |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
api_base="https://your-endpoint.openai.azure.com/",
api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any
model: str = "text-embedding-ada-002"
deployment: str = model
openai_api_version: Optional[str] = None
openai_api_base: Optional[str] = None
openai_api_type: Optional[str] = None
embedding_ctx_length: int = 8191
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout in seconds for the OpenAPI request."""
headers: Any = None
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | """Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
openai_api_type = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
if openai_api_type in ("azure", "azure_ad", "azuread"):
default_api_version = "2022-12-01"
else:
default_api_version = ""
openai_api_version = get_from_dict_or_env(
values,
"openai_api_version", |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | "OPENAI_API_VERSION",
default=default_api_version,
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
if openai_api_base:
openai.api_base = openai_api_base
if openai_api_type:
openai.api_version = openai_api_version
if openai_api_type:
openai.api_type = openai_api_type
values["client"] = openai.Embedding
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
return values
def _get_len_safe_embeddings( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
encoding = tiktoken.model.encoding_for_model(self.model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens += [token[j : j + self.embedding_ctx_length]]
indices += [i]
batched_embeddings = [] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | _chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
engine=self.deployment,
request_timeout=self.request_timeout,
headers=self.headers,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = embed_with_retry(
self,
input="",
engine=self.deployment,
request_timeout=self.request_timeout,
headers=self.headers,
)["data"][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
def _embedding_func(self, text: str, *, engine: str) -> List[float]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | """Call out to OpenAI's embedding endpoint."""
if len(text) > self.embedding_ctx_length:
return self._get_len_safe_embeddings([text], engine=engine)[0]
else:
if self.model.endswith("001"):
text = text.replace("\n", " ")
return embed_with_retry(
self,
input=[text],
engine=engine,
request_timeout=self.request_timeout,
headers=self.headers,
)["data"][0]["embedding"]
def embed_documents( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/embeddings/openai.py | self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
return self._get_len_safe_embeddings(texts, engine=self.deployment)
def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = self._embedding_func(text, engine=self.deployment)
return embedding |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/llms/openai.py | """Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/llms/openai.py | retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import BaseLLM
from langchain.schema import Generation, LLMResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
"""Update response from the stream response."""
response["choices"][0]["text"] += stream_response["choices"][0]["text"]
response["choices"][0]["finish_reason"] = stream_response["choices"][0][
"finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,243 | Add possibility to set a proxy for openai API access | ### Feature request
For a deployment behind a corporate proxy, it's useful to be able to access the API by specifying an explicit proxy.
### Motivation
Currently it's possible to do this by setting the environment variables http_proxy / https_proxy to set a proxy for the whole python interpreter. However this then prevents access to other internal servers. accessing other network resources (e.g. a vector database on a different server, corporate S3 storage etc.) should not go through the proxy. So it's important to be able to just proxy requests for externally hosted APIs. We are working with the OpenAI API and currently we cannot both access those and our qdrant database on another server.
### Your contribution
Since the openai python package supports the proxy parameter, this is relatively easy to implement for the OpenAI API. I'll submit a PR. | https://github.com/langchain-ai/langchain/issues/5243 | https://github.com/langchain-ai/langchain/pull/5246 | 9c0cb90997db9eb2e2a736df458d39fd7bec8ffb | 88ed8e1cd6c7f1b679efe9e80cf6f8c33e3e6217 | "2023-05-25T13:00:09Z" | python | "2023-05-25T16:50:25Z" | langchain/llms/openai.py | return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any: |
Subsets and Splits