parametrize redis (#2946)

langchain/vectorstores/redis.py

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