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community[minor]: Pathway vectorstore(#14859)

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- **Description:** Integration with pathway.com data processing pipeline
acting as an always updated vectorstore
  - **Issue:** not applicable
- **Dependencies:** optional dependency on
[`pathway`](https://pypi.org/project/pathway/)
  - **Twitter handle:** pathway_com

The PR provides and integration with `pathway` to provide an easy to use
always updated vector store:

```python
import pathway as pw
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import PathwayVectorClient, PathwayVectorServer

data_sources = []
data_sources.append(
    pw.io.gdrive.read(object_id="17H4YpBOAKQzEJ93xmC2z170l0bP2npMy", service_user_credentials_file="credentials.json", with_metadata=True))

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
embeddings_model = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])
vector_server = PathwayVectorServer(
    *data_sources,
    embedder=embeddings_model,
    splitter=text_splitter,
)
vector_server.run_server(host="127.0.0.1", port="8765", threaded=True, with_cache=False)
client = PathwayVectorClient(
    host="127.0.0.1",
    port="8765",
)
query = "What is Pathway?"
docs = client.similarity_search(query)
```

The `PathwayVectorServer` builds a data processing pipeline which
continusly scans documents in a given source connector (google drive,
s3, ...) and builds a vector store. The `PathwayVectorClient` implements
LangChain's `VectorStore` interface and connects to the server to
retrieve documents.

---------

Co-authored-by: Mateusz Lewandowski <lewymati@users.noreply.github.com>
Co-authored-by: mlewandowski <mlewandowski@MacBook-Pro-mlewandowski.local>
Co-authored-by: Berke <berkecanrizai1@gmail.com>
Co-authored-by: Adrian Kosowski <adrian@pathway.com>
Co-authored-by: mlewandowski <mlewandowski@macbook-pro-mlewandowski.home>
Co-authored-by: berkecanrizai <63911408+berkecanrizai@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: mlewandowski <mlewandowski@MBPmlewandowski.ht.home>
Co-authored-by: Szymon Dudycz <szymond@pathway.com>
Co-authored-by: Szymon Dudycz <szymon.dudycz@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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191
docs/docs/integrations/vectorstores/pathway.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pathway\n",
"> [Pathway](https://pathway.com/) is an open data processing framework. It allows you to easily develop data transformation pipelines and Machine Learning applications that work with live data sources and changing data.\n",
"\n",
"This notebook demonstrates how to use a live `Pathway` data indexing pipeline with `Langchain`. You can query the results of this pipeline from your chains in the same manner as you would a regular vector store. However, under the hood, Pathway updates the index on each data change giving you always up-to-date answers.\n",
"\n",
"In this notebook, we will use a [public demo document processing pipeline](https://pathway.com/solutions/ai-pipelines#try-it-out) that:\n",
"\n",
"1. Monitors several cloud data sources for data changes.\n",
"2. Builds a vector index for the data.\n",
"\n",
"To have your own document processing pipeline check the [hosted offering](https://pathway.com/solutions/ai-pipelines) or [build your own](https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/).\n",
"\n",
"We will connect to the index using a `VectorStore` client, which implements the `similarity_search` function to retrieve matching documents.\n",
"\n",
"The basic pipeline used in this document allows to effortlessly build a simple vector index of files stored in a cloud location. However, Pathway provides everything needed to build realtime data pipelines and apps, including SQL-like able operations such as groupby-reductions and joins between disparate data sources, time-based grouping and windowing of data, and a wide array of connectors.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying the data pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To instantiate and configure the client you need to provide either the `url` or the `host` and `port` of your document indexing pipeline. In the code below we use a publicly available [demo pipeline](https://pathway.com/solutions/ai-pipelines#try-it-out), which REST API you can access at `https://demo-document-indexing.pathway.stream`. This demo ingests documents from [Google Drive](https://drive.google.com/drive/u/0/folders/1cULDv2OaViJBmOfG5WB0oWcgayNrGtVs) and [Sharepoint](https://navalgo.sharepoint.com/sites/ConnectorSandbox/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2FConnectorSandbox%2FShared%20Documents%2FIndexerSandbox&p=true&ga=1) and maintains an index for retrieving documents."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import PathwayVectorClient\n",
"\n",
"client = PathwayVectorClient(url=\"https://demo-document-indexing.pathway.stream\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" And we can start asking queries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"query = \"What is Pathway?\"\n",
"docs = client.similarity_search(query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" **Your turn!** [Get your pipeline](https://pathway.com/solutions/ai-pipelines) or upload [new documents](https://chat-realtime-sharepoint-gdrive.demo.pathway.com/) to the demo pipeline and retry the query!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering based on file metadata\n",
"\n",
"We support document filtering using [jmespath](https://jmespath.org/) expressions, for instance:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# take into account only sources modified later than unix timestamp\n",
"docs = client.similarity_search(query, metadata_filter=\"modified_at >= `1702672093`\")\n",
"\n",
"# take into account only sources modified later than unix timestamp\n",
"docs = client.similarity_search(query, metadata_filter=\"owner == `james`\")\n",
"\n",
"# take into account only sources with path containing 'repo_readme'\n",
"docs = client.similarity_search(query, metadata_filter=\"contains(path, 'repo_readme')\")\n",
"\n",
"# and of two conditions\n",
"docs = client.similarity_search(\n",
" query, metadata_filter=\"owner == `james` && modified_at >= `1702672093`\"\n",
")\n",
"\n",
"# or of two conditions\n",
"docs = client.similarity_search(\n",
" query, metadata_filter=\"owner == `james` || modified_at >= `1702672093`\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting information on indexed files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" `PathwayVectorClient.get_vectorstore_statistics()` gives essential statistics on the state of the vector store, like the number of indexed files and the timestamp of last updated one. You can use it in your chains to tell the user how fresh is your knowledge base."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client.get_vectorstore_statistics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Your own pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running in production\n",
"To have your own Pathway data indexing pipeline check the Pathway's offer for [hosted pipelines](https://pathway.com/solutions/ai-pipelines). You can also run your own Pathway pipeline - for information on how to build the pipeline refer to [Pathway guide](https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Processing documents\n",
"\n",
"The vectorization pipeline supports pluggable components for parsing, splitting and embedding documents. For embedding and splitting you can use [Langchain components](https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/#langchain) or check [embedders](https://pathway.com/developers/api-docs/pathway-xpacks-llm/embedders) and [splitters](https://pathway.com/developers/api-docs/pathway-xpacks-llm/splitters) available in Pathway. If parser is not provided, it defaults to `UTF-8` parser. You can find available parsers [here](https://github.com/pathwaycom/pathway/blob/main/python/pathway/xpacks/llm/parser.py)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

1
libs/community/langchain_community/vectorstores/__init__.py
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@ -79,6 +79,7 @@ _module_lookup = {
"Neo4jVector": "langchain_community.vectorstores.neo4j_vector",
"NeuralDBVectorStore": "langchain_community.vectorstores.thirdai_neuraldb",
"OpenSearchVectorSearch": "langchain_community.vectorstores.opensearch_vector_search", # noqa: E501
"PathwayVectorClient": "langchain_community.vectorstores.pathway",
"PGEmbedding": "langchain_community.vectorstores.pgembedding",
"PGVector": "langchain_community.vectorstores.pgvector",
"Pinecone": "langchain_community.vectorstores.pinecone",

228
libs/community/langchain_community/vectorstores/pathway.py
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@ -0,0 +1,228 @@
"""
Pathway Vector Store client.
The Pathway Vector Server is a pipeline written in the Pathway framweork which indexes
all files in a given folder, embeds them, and builds a vector index. The pipeline reacts
to changes in source files, automatically updating appropriate index entries.
The PathwayVectorClient implements the LangChain VectorStore interface and queries the
PathwayVectorServer to retrieve up-to-date documents.
You can use the client with managed instances of Pathway Vector Store, or run your own
instance as described at https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/
"""
import json
import logging
from typing import Any, Callable, Iterable, List, Optional, Tuple
import requests
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
# Copied from https://github.com/pathwaycom/pathway/blob/main/python/pathway/xpacks/llm/vector_store.py
# to remove dependency on Pathway library.
class _VectorStoreClient:
def __init__(
self,
host: Optional[str] = None,
port: Optional[int] = None,
url: Optional[str] = None,
):
"""
A client you can use to query :py:class:`VectorStoreServer`.
Please provide aither the `url`, or `host` and `port`.
Args:
- host: host on which `:py:class:`VectorStoreServer` listens
- port: port on which `:py:class:`VectorStoreServer` listens
- url: url at which `:py:class:`VectorStoreServer` listens
"""
err = "Either (`host` and `port`) or `url` must be provided, but not both."
if url is not None:
if host or port:
raise ValueError(err)
self.url = url
else:
if host is None:
raise ValueError(err)
port = port or 80
self.url = f"http://{host}:{port}"
def query(
self, query: str, k: int = 3, metadata_filter: Optional[str] = None
) -> List[dict]:
"""
Perform a query to the vector store and fetch results.
Args:
- query:
- k: number of documents to be returned
- metadata_filter: optional string representing the metadata filtering query
in the JMESPath format. The search will happen only for documents
satisfying this filtering.
"""
data = {"query": query, "k": k}
if metadata_filter is not None:
data["metadata_filter"] = metadata_filter
url = self.url + "/v1/retrieve"
response = requests.post(
url,
data=json.dumps(data),
headers={"Content-Type": "application/json"},
timeout=3,
)
responses = response.json()
return sorted(responses, key=lambda x: x["dist"])
# Make an alias
__call__ = query
def get_vectorstore_statistics(self) -> dict:
"""Fetch basic statistics about the vector store."""
url = self.url + "/v1/statistics"
response = requests.post(
url,
json={},
headers={"Content-Type": "application/json"},
)
responses = response.json()
return responses
def get_input_files(
self,
metadata_filter: Optional[str] = None,
filepath_globpattern: Optional[str] = None,
) -> list:
"""
Fetch information on documents in the the vector store.
Args:
metadata_filter: optional string representing the metadata filtering query
in the JMESPath format. The search will happen only for documents
satisfying this filtering.
filepath_globpattern: optional glob pattern specifying which documents
will be searched for this query.
"""
url = self.url + "/v1/inputs"
response = requests.post(
url,
json={
"metadata_filter": metadata_filter,
"filepath_globpattern": filepath_globpattern,
},
headers={"Content-Type": "application/json"},
)
responses = response.json()
return responses
class PathwayVectorClient(VectorStore):
"""
VectorStore connecting to Pathway Vector Store.
"""
def __init__(
self,
host: Optional[str] = None,
port: Optional[int] = None,
url: Optional[str] = None,
) -> None:
"""
A client you can use to query Pathway Vector Store.
Please provide aither the `url`, or `host` and `port`.
Args:
- host: host on which Pathway Vector Store listens
- port: port on which Pathway Vector Store listens
- url: url at which Pathway Vector Store listens
"""
self.client = _VectorStoreClient(host, port, url)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Pathway is not suitable for this method."""
raise NotImplementedError(
"Pathway vector store does not support adding or removing texts"
" from client."
)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> "PathwayVectorClient":
raise NotImplementedError(
"Pathway vector store does not support initializing from_texts."
)
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
metadata_filter = kwargs.pop("metadata_filter", None)
if kwargs:
logging.warning(
"Unknown kwargs passed to PathwayVectorClient.similarity_search: %s",
kwargs,
)
rets = self.client(query=query, k=k, metadata_filter=metadata_filter)
return [
Document(page_content=ret["text"], metadata=ret["metadata"]) for ret in rets
]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
metadata_filter: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Pathway with distance.
Args:
- query (str): Query text to search for.
- k (int): Number of results to return. Defaults to 4.
- metadata_filter (Optional[str]): Filter by metadata.
Filtering query should be in JMESPath format. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
"""
rets = self.client(query=query, k=k, metadata_filter=metadata_filter)
return [
(Document(page_content=ret["text"], metadata=ret["metadata"]), ret["dist"])
for ret in rets
]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
return self._cosine_relevance_score_fn
def get_vectorstore_statistics(self) -> dict:
"""Fetch basic statistics about the Vector Store."""
return self.client.get_vectorstore_statistics()
def get_input_files(
self,
metadata_filter: Optional[str] = None,
filepath_globpattern: Optional[str] = None,
) -> list:
"""List files indexed by the Vector Store."""
return self.client.get_input_files(metadata_filter, filepath_globpattern)

1
libs/community/tests/unit_tests/vectorstores/test_imports.py
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@ -10,6 +10,7 @@ def test_all_imports() -> None:
"AlibabaCloudOpenSearchSettings",
"ClickhouseSettings",
"MyScaleSettings",
"PathwayVectorClient",
"DistanceStrategy",
"KineticaSettings",
]:

1
libs/community/tests/unit_tests/vectorstores/test_public_api.py
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@ -57,6 +57,7 @@ _EXPECTED = [
"OpenSearchVectorSearch",
"PGEmbedding",
"PGVector",
"PathwayVectorClient",
"Pinecone",
"Qdrant",
"Redis",

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