docs(vectordbs): document missing config params (timezone, service_account_json, additional_headers, use_ssl/ca_certs, embedding_model_dims)

2026-06-25 14:03:14 +05:30
parent 8f0516e033
commit 4e2161dbfe
5 changed files with 7 additions and 1 deletions
@@ -56,6 +56,8 @@ Here are the parameters available for configuring Elasticsearch:
 | `api_key`              | API key for authentication                         | `None`        |
 | `user`                 | Username for basic authentication                  | `None`        |
 | `password`             | Password for basic authentication                  | `None`        |
+| `use_ssl`              | Whether to use SSL for the connection              | `True`        |
+| `ca_certs`             | Path to CA bundle for SSL certificate verification | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
 | `custom_search_query`  | Function returning a custom search query           | `None`        |
@@ -55,6 +55,7 @@ Here are the parameters available for configuring FAISS:
 | `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
 | `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
 | `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |

 ### Performance Considerations

@@ -51,6 +51,7 @@ Here are the parameters available for configuring Valkey:
 | `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
 | `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
 | `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
+| `timezone` | Timezone for timestamp handling | `UTC` |

 ## Cluster Mode

@@ -47,3 +47,4 @@ m.add("Your text here", user_id="user", metadata={"category": "example"})
 | `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
 | `region` | Google Cloud region | Yes |
 | `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
+| `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` |
@@ -48,4 +48,5 @@ Here are the parameters available for configuring Weaviate:
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `cluster_url` | URL for the Weaviate server | `None` |
-| `auth_client_secret` | API key for Weaviate authentication | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |
+| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |