forked from Archives/langchain
parametrize redis (#2946)
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@ -70,6 +70,9 @@ class Redis(VectorStore):
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redis_url: str,
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index_name: str,
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embedding_function: Callable,
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content_key: str = "content",
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metadata_key: str = "metadata",
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vector_key: str = "content_vector",
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**kwargs: Any,
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):
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"""Initialize with necessary components."""
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@ -92,6 +95,9 @@ class Redis(VectorStore):
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raise ValueError(f"Redis failed to connect: {e}")
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self.client = redis_client
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self.content_key = content_key
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self.metadata_key = metadata_key
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self.vector_key = vector_key
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def add_texts(
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self,
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@ -112,11 +118,11 @@ class Redis(VectorStore):
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pipeline.hset(
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key,
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mapping={
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"content": text,
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"content_vector": np.array(
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self.content_key: text,
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self.vector_key: np.array(
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self.embedding_function(text), dtype=np.float32
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).tobytes(),
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"metadata": json.dumps(metadata),
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self.metadata_key: json.dumps(metadata),
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},
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)
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ids.append(key)
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@ -191,8 +197,8 @@ class Redis(VectorStore):
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embedding = self.embedding_function(query)
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# Prepare the Query
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return_fields = ["metadata", "content", "vector_score"]
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vector_field = "content_vector"
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return_fields = [self.metadata_key, self.content_key, "vector_score"]
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vector_field = self.vector_key
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hybrid_fields = "*"
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base_query = (
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f"{hybrid_fields}=>[KNN {k} @{vector_field} $vector AS vector_score]"
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@ -232,6 +238,9 @@ class Redis(VectorStore):
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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index_name: Optional[str] = None,
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content_key: str = "content",
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metadata_key: str = "metadata",
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vector_key: str = "content_vector",
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**kwargs: Any,
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) -> Redis:
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"""Construct RediSearch wrapper from raw documents.
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@ -287,10 +296,10 @@ class Redis(VectorStore):
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"COSINE" # distance metric for the vectors (ex. COSINE, IP, L2)
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)
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schema = (
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TextField(name="content"),
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TextField(name="metadata"),
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TextField(name=content_key),
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TextField(name=metadata_key),
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VectorField(
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"content_vector",
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vector_key,
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"FLAT",
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{
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"TYPE": "FLOAT32",
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@ -313,11 +322,9 @@ class Redis(VectorStore):
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pipeline.hset(
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key,
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mapping={
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"content": text,
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"content_vector": np.array(
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embeddings[i], dtype=np.float32
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).tobytes(),
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"metadata": json.dumps(metadata),
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content_key: text,
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vector_key: np.array(embeddings[i], dtype=np.float32).tobytes(),
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metadata_key: json.dumps(metadata),
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},
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)
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pipeline.execute()
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