make similarityResponse object arguments and not positional (#2930)

* make `similarityResponse` object arguments and not positional

* reuse client for qdrant

server/utils/helpers/chat/index.js

@@ -371,7 +371,7 @@ function cannonball({
  *
  * @param {Object} config - params to call
  * @param {object} config.nDocs = fill size of the window
- * @param {object} config.searchResults = vector similarityResponse results for .sources
+ * @param {object} config.searchResults = vector `similarityResponse` results for .sources
  * @param {object[]} config.history - rawHistory of chat containing sources
  * @param {string[]} config.filterIdentifiers - Pinned document identifiers to prevent duplicate context
  * @returns {{

server/utils/vectorDbProviders/astra/index.js

@@ -269,14 +269,14 @@ const AstraDB = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };
@@ -287,14 +287,14 @@ const AstraDB = {
       message: false,
     };
   },
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],

server/utils/vectorDbProviders/chroma/index.js

@@ -108,14 +108,14 @@ const Chroma = {
     const namespace = await this.namespace(client, this.normalize(_namespace));
     return namespace?.vectorCount || 0;
   },
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const collection = await client.getCollection({
       name: this.normalize(namespace),
     });
@@ -356,14 +356,14 @@ const Chroma = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { metadata: { ...metadata, text: contextTexts[i] } };

server/utils/vectorDbProviders/lance/index.js

@@ -59,22 +59,23 @@ const LanceDb = {
   },
   /**
    * Performs a SimilaritySearch on a give LanceDB namespace.
-   * @param {LanceClient} client
-   * @param {string} namespace
-   * @param {number[]} queryVector
-   * @param {number} similarityThreshold
-   * @param {number} topN
-   * @param {string[]} filterIdentifiers
+   * @param {Object} params
+   * @param {LanceClient} params.client
+   * @param {string} params.namespace
+   * @param {number[]} params.queryVector
+   * @param {number} params.similarityThreshold
+   * @param {number} params.topN
+   * @param {string[]} params.filterIdentifiers
    * @returns
    */
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const collection = await client.openTable(namespace);
     const result = {
       contextTexts: [],
@@ -313,14 +314,14 @@ const LanceDb = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { metadata: { ...metadata, text: contextTexts[i] } };

server/utils/vectorDbProviders/milvus/index.js

@@ -312,14 +312,14 @@ const Milvus = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };
@@ -330,14 +330,14 @@ const Milvus = {
       message: false,
     };
   },
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],

server/utils/vectorDbProviders/pinecone/index.js

@@ -36,21 +36,21 @@ const PineconeDB = {
     const namespace = await this.namespace(pineconeIndex, _namespace);
     return namespace?.recordCount || 0;
   },
-  similarityResponse: async function (
-    index,
+  similarityResponse: async function ({
+    client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],
       scores: [],
     };
 
-    const pineconeNamespace = index.namespace(namespace);
+    const pineconeNamespace = client.namespace(namespace);
     const response = await pineconeNamespace.query({
       vector: queryVector,
       topK: topN,
@@ -254,14 +254,14 @@ const PineconeDB = {
       );
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
-      pineconeIndex,
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
+      client: pineconeIndex,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };

server/utils/vectorDbProviders/qdrant/index.js

@@ -47,15 +47,14 @@ const QDrant = {
     const namespace = await this.namespace(client, _namespace);
     return namespace?.vectorCount || 0;
   },
-  similarityResponse: async function (
-    _client,
+  similarityResponse: async function ({
+    client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
-    const { client } = await this.connect();
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],
@@ -338,14 +337,14 @@ const QDrant = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };

server/utils/vectorDbProviders/weaviate/index.js

@@ -74,14 +74,14 @@ const Weaviate = {
       return 0;
     }
   },
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],
@@ -381,14 +381,14 @@ const Weaviate = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };

server/utils/vectorDbProviders/zilliz/index.js

@@ -305,14 +305,14 @@ const Zilliz = {
     }
 
     const queryVector = await LLMConnector.embedTextInput(input);
-    const { contextTexts, sourceDocuments } = await this.similarityResponse(
+    const { contextTexts, sourceDocuments } = await this.similarityResponse({
       client,
       namespace,
       queryVector,
       similarityThreshold,
       topN,
-      filterIdentifiers
-    );
+      filterIdentifiers,
+    });
 
     const sources = sourceDocuments.map((metadata, i) => {
       return { ...metadata, text: contextTexts[i] };
@@ -323,14 +323,14 @@ const Zilliz = {
       message: false,
     };
   },
-  similarityResponse: async function (
+  similarityResponse: async function ({
     client,
     namespace,
     queryVector,
     similarityThreshold = 0.25,
     topN = 4,
-    filterIdentifiers = []
-  ) {
+    filterIdentifiers = [],
+  }) {
     const result = {
       contextTexts: [],
       sourceDocuments: [],