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from pathlib import Path |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain_community.vectorstores.redis import Redis |
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store |
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from langflow.helpers.data import docs_to_data |
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from langflow.io import DataInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput |
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from langflow.schema import Data |
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class RedisVectorStoreComponent(LCVectorStoreComponent): |
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"""A custom component for implementing a Vector Store using Redis.""" |
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display_name: str = "Redis" |
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description: str = "Implementation of Vector Store using Redis" |
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis" |
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name = "Redis" |
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icon = "Redis" |
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inputs = [ |
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SecretStrInput(name="redis_server_url", display_name="Redis Server Connection String", required=True), |
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StrInput( |
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name="redis_index_name", |
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display_name="Redis Index", |
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), |
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StrInput(name="code", display_name="Code", advanced=True), |
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StrInput( |
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name="schema", |
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display_name="Schema", |
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), |
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MultilineInput(name="search_query", display_name="Search Query"), |
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DataInput( |
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name="ingest_data", |
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display_name="Ingest Data", |
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is_list=True, |
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), |
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IntInput( |
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name="number_of_results", |
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display_name="Number of Results", |
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info="Number of results to return.", |
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value=4, |
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advanced=True, |
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), |
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HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self) -> Redis: |
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documents = [] |
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for _input in self.ingest_data or []: |
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if isinstance(_input, Data): |
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documents.append(_input.to_lc_document()) |
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else: |
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documents.append(_input) |
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Path("docuemnts.txt").write_text(str(documents), encoding="utf-8") |
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if not documents: |
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if self.schema is None: |
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msg = "If no documents are provided, a schema must be provided." |
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raise ValueError(msg) |
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redis_vs = Redis.from_existing_index( |
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embedding=self.embedding, |
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index_name=self.redis_index_name, |
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schema=self.schema, |
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key_prefix=None, |
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redis_url=self.redis_server_url, |
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) |
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else: |
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) |
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docs = text_splitter.split_documents(documents) |
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redis_vs = Redis.from_documents( |
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documents=docs, |
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embedding=self.embedding, |
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redis_url=self.redis_server_url, |
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index_name=self.redis_index_name, |
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) |
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return redis_vs |
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def search_documents(self) -> list[Data]: |
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vector_store = self.build_vector_store() |
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if self.search_query and isinstance(self.search_query, str) and self.search_query.strip(): |
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docs = vector_store.similarity_search( |
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query=self.search_query, |
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k=self.number_of_results, |
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) |
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data = docs_to_data(docs) |
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self.status = data |
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return data |
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return [] |
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