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env variable based fix for non chatgpt embeddings (#3964)

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this is a simple fix for #2219 

i also added some documentation for this environment variable
This commit is contained in:
Martin Holzhauer
2023-05-06 01:31:38 +02:00
committed by C.J. Jameson
parent 3c6fa9126a
commit 97abe337ba
2 changed files with 7 additions and 2 deletions
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4
docs/integrations/pgvector.md
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@@ -24,6 +24,10 @@ To import this vectorstore:
from langchain.vectorstores.pgvector import PGVector
```
PGVector embedding size is not autodetected. If you are using ChatGPT or any other embedding with 1536 dimensions
default is fine. If you are going to use for example HuggingFaceEmbeddings you need to set the environment variable named `PGVECTOR_VECTOR_SIZE`
to the needed value, In case of HuggingFaceEmbeddings is would be: `PGVECTOR_VECTOR_SIZE=768`
### Usage
For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pgvector.ipynb)

5
langchain/vectorstores/pgvector.py
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@@ -3,6 +3,7 @@ from __future__ import annotations
import enum
import logging
import os
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
@@ -19,7 +20,7 @@ from langchain.vectorstores.base import VectorStore
Base = declarative_base() # type: Any
ADA_TOKEN_COUNT = 1536
PGVECTOR_VECTOR_SIZE = int(os.getenv("PGVECTOR_VECTOR_SIZE", default="1536"))
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
@@ -79,7 +80,7 @@ class EmbeddingStore(BaseModel):
)
collection = relationship(CollectionStore, back_populates="embeddings")
embedding: Vector = sqlalchemy.Column(Vector(ADA_TOKEN_COUNT))
embedding: Vector = sqlalchemy.Column(Vector(PGVECTOR_VECTOR_SIZE))
document = sqlalchemy.Column(sqlalchemy.String, nullable=True)
cmetadata = sqlalchemy.Column(JSON, nullable=True)