Add RAG example for AI connector + delete action for vector index (#31)

* Added a RAG example for AI connector

* Added delete option to vectordb

* Changed id to ids

docs/components/langchain_vector_store_embedding_index.md

@@ -41,13 +41,20 @@ component_config:
       <freeform-object>
     },
     ...
-  ]
+  ],
+  ids: [
+    <string>,
+    ...
+  ],
+  action:   <string>
 }
 ```
 | Field | Required | Description |
 | --- | --- | --- |
 | texts | True |  |
 | metadatas | False |  |
+| ids | False | The ID of the text to add to the index. required for 'delete' action |
+| action | False | The action to perform on the index from one of 'add', 'delete' |
 
 
 ## Component Output Schema

docs/components/langchain_vector_store_embedding_search.md

@@ -28,7 +28,7 @@ component_config:
 | embedding_component_path | True |  | The embedding library path - e.g. 'langchain_community.embeddings' |
 | embedding_component_name | True |  | The embedding model to use - e.g. BedrockEmbeddings |
 | embedding_component_config | True |  | Model specific configuration for the embedding model. See documentation for valid parameter names. |
-| max_results | True |  | The maximum number of results to return |
+| max_results | True | 3 | The maximum number of results to return |
 | combine_context_from_same_source | False | True | Set to False if you don't want to combine all the context from the same source. Default is True |
 
 

examples/llm/openai_chat.yaml

@@ -16,7 +16,7 @@
 # required ENV variables:
 # - OPENAI_API_KEY
 # - OPENAI_API_ENDPOINT
-# - MODEL_NAME
+# - OPENAI_MODEL_NAME
 # - SOLACE_BROKER_URL
 # - SOLACE_BROKER_USERNAME
 # - SOLACE_BROKER_PASSWORD
@@ -61,7 +61,7 @@ flows:
         component_config:
           api_key: ${OPENAI_API_KEY}
           base_url: ${OPENAI_API_ENDPOINT}
-          model: ${MODEL_NAME}
+          model: ${OPENAI_MODEL_NAME}
           temperature: 0.01
         input_transforms:
           - type: copy

examples/llm/openai_chroma_rag.yaml

@@ -0,0 +1,183 @@
+# OpenAI RAG (Retrieval Augmented Generation) example using ChromaDB
+# This will create 2 flows like these:
+#
+# Solace[topic:demo/rag/data] -> embed and store in ChromaDB
+# Solace[topic:demo/rag/query] -> search in ChromaDB -> OpenAI -> Solace[topic:demo/rag/query/response]
+#
+# Load Data:
+# Send data to Solace topic `demo/rag/data` with the following payload format:
+# {
+#   "texts": [<text data 1>. <text data 2>, ...]
+# }
+#
+# RAG Query:
+# Send query to Solace topic `demo/rag/query` with the following payload format:
+# {
+#   "query": "<question or request as text>"
+# }
+# The response will be sent to Solace topic `demo/rag/query/response`
+#
+# Dependencies:
+# pip install -U langchain_openai openai chromadb langchain-chroma
+#
+# Required ENV variables:
+# - OPENAI_API_KEY
+# - OPENAI_API_ENDPOINT
+# - OPENAI_EMBEDDING_MODEL_NAME
+# - OPENAI_MODEL_NAME
+# - SOLACE_BROKER_URL
+# - SOLACE_BROKER_USERNAME
+# - SOLACE_BROKER_PASSWORD
+# - SOLACE_BROKER_VPN
+
+---
+log:
+  stdout_log_level: INFO
+  log_file_level: INFO
+  log_file: solace_ai_connector.log
+
+shared_config:
+  - broker_config: &broker_connection
+      broker_type: solace
+      broker_url: ${SOLACE_BROKER_URL}
+      broker_username: ${SOLACE_BROKER_USERNAME}
+      broker_password: ${SOLACE_BROKER_PASSWORD}
+      broker_vpn: ${SOLACE_BROKER_VPN}
+
+# Data ingestion and augmented inference flows
+flows:
+  # Data ingestion to chromaDB for RAG
+  - name: chroma_ingest
+    components:
+      # Data Input from a Solace broker for ingestion
+      - component_name: solace_data_input
+        component_module: broker_input
+        component_config:
+          <<: *broker_connection
+          broker_queue_name: demo_rag_data
+          broker_subscriptions:
+            - topic: demo/rag/data
+              qos: 1
+          payload_encoding: utf-8
+          payload_format: json
+
+      # Embedding data & ChromaDB ingest
+      - component_name: chroma_embed
+        component_module: langchain_vector_store_embedding_index
+        component_config:
+          vector_store_component_path: langchain_chroma
+          vector_store_component_name: Chroma
+          vector_store_component_config:
+            persist_directory: ./chroma_data
+            collection_name: rag
+          embedding_component_path: langchain_openai
+          embedding_component_name: OpenAIEmbeddings
+          embedding_component_config:
+            api_key: ${OPENAI_API_KEY}
+            base_url: ${OPENAI_API_ENDPOINT}
+            model: ${OPENAI_EMBEDDING_MODEL_NAME}
+        input_transforms:
+          - type: copy
+            source_value: topic:demo/rag/data
+            dest_expression: user_data.vector_input:metadatas.source
+          - type: copy
+            source_expression: input.payload:texts
+            dest_expression: user_data.vector_input:texts
+        input_selection:
+          source_expression: user_data.vector_input
+
+  # RAG Inference flow
+  - name: OpenAI_RAG
+    components:
+      # Inference Input from a Solace broker for completion
+      - component_name: solace_completion_broker
+        component_module: broker_input
+        component_config:
+          <<: *broker_connection
+          broker_queue_name: demo_rag_query
+          broker_subscriptions:
+            - topic: demo/rag/query
+              qos: 1
+          payload_encoding: utf-8
+          payload_format: json
+
+      # Retrieve the top-k documents from ChromaDB
+      - component_name: chroma_search
+        component_module: langchain_vector_store_embedding_search
+        component_config:
+          vector_store_component_path: langchain_chroma
+          vector_store_component_name: Chroma
+          vector_store_component_config:
+            persist_directory: ./chroma_data
+            collection_name: rag
+          embedding_component_path: langchain_openai
+          embedding_component_name: OpenAIEmbeddings
+          embedding_component_config:
+            api_key: ${OPENAI_API_KEY}
+            base_url: ${OPENAI_API_ENDPOINT}
+            model: ${OPENAI_EMBEDDING_MODEL_NAME}
+          max_results: 5
+        input_transforms:
+          - type: copy
+            source_expression: input.payload:query
+            dest_expression: user_data.vector_input:text
+        input_selection:
+          source_expression:  user_data.vector_input
+
+      # Generate response using the retrieved data
+      - component_name: llm_request
+        component_module: openai_chat_model
+        component_config:
+          api_key: ${OPENAI_API_KEY}
+          base_url: ${OPENAI_API_ENDPOINT}
+          model: ${OPENAI_MODEL_NAME}
+          temperature: 0.01
+        input_transforms:
+          # Extract and format the retrieved data
+          - type: map
+            source_list_expression: previous:result
+            source_expression: |
+              template:{{text://item:text}}\n\n
+            dest_list_expression: user_data.retrieved_data
+
+          - type: copy
+            source_expression: |
+              template:You are a helpful AI assistant. Using the provided context, help with the user's request below. Refrain to use any knowledge outside from the provided context. If the user query can not be answered using the provided context, reject user's query.
+
+              <context>
+              {{text://user_data.retrieved_data}}
+              </context>
+              
+              <user-question>
+              {{text://input.payload:query}}
+              </user-question>
+            dest_expression: user_data.llm_input:messages.0.content
+          - type: copy
+            source_expression: static:user
+            dest_expression: user_data.llm_input:messages.0.role
+        input_selection:
+          source_expression: user_data.llm_input
+
+      # Send response back to broker with completion and retrieved data
+      - component_name: send_response
+        component_module: broker_output
+        component_config:
+          <<: *broker_connection
+          payload_encoding: utf-8
+          payload_format: json
+          copy_user_properties: true
+        input_transforms:
+          - type: copy
+            source_expression: previous:content
+            dest_expression: user_data.output:payload.response
+          - type: copy
+            source_expression: input.payload:query
+            dest_expression: user_data.output:payload.query
+          - type: copy
+            source_expression: user_data.retrieved_data
+            dest_expression: user_data.output:payload.retrieved_data
+          - type: copy
+            source_expression: template:{{text://input.topic}}/response
+            dest_expression: user_data.output:topic
+        input_selection:
+          source_expression: user_data.output

examples/milvus_store.yaml

@@ -51,10 +51,10 @@ flows:
               invoke:
                 module: platform
                 function: system
-            dest_expression: user_data.vector_input:metadata.system
+            dest_expression: user_data.vector_input:metadatas.system
           - type: copy
             source_value: username
-            dest_expression: user_data.vector_input:metadata.user
+            dest_expression: user_data.vector_input:metadatas.user
           - type: copy
             source_expression: input.payload:text
             dest_expression: user_data.vector_input:texts

src/solace_ai_connector/components/general/langchain/langchain_vector_store_embedding_base.py

@@ -78,8 +78,10 @@ def init(self):
                 self.vector_store_info["config"], vector_store_class
             )
         except Exception:  # pylint: disable=broad-except
-            del self.vector_store_info["config"]["embeddings"]
-            del self.vector_store_info["config"]["embedding_function"]
+            if "embeddings" in self.vector_store_info["config"]:
+                del self.vector_store_info["config"]["embeddings"]
+            if "embedding_function" in self.vector_store_info["config"]:
+                del self.vector_store_info["config"]["embedding_function"]
             self.vector_store = vector_store_class.from_texts(
                 [], self.embedding, **self.vector_store_info["config"]
             )

src/solace_ai_connector/components/general/langchain/langchain_vector_store_embedding_index.py

@@ -74,6 +74,18 @@
                     "type": "object",
                 },
             },
+            "ids": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                },
+                "description": "The ID of the text to add to the index. required for 'delete' action",
+            },
+            "action": {
+                "type": "string",
+                "default": "add",
+                "description": "The action to perform on the index from one of 'add', 'delete'",
+            },
         },
         "required": ["texts"],
     },
@@ -116,12 +128,35 @@ def invoke(self, message, data):
 
         # Get the metadatas if they exist
         metadatas = data.get("metadatas", None)
-        args = [texts]
         if metadatas is not None:
             if not isinstance(metadatas, list):
                 metadatas = [metadatas]
-            args.append(metadatas)
 
+        # Get the ids if they exist
+        ids = data.get("ids", None)
+        if ids is not None:
+            if not isinstance(ids, list):
+                ids = [ids]
+
+        action = data.get("action", "add")
+        match action:
+            case "add":
+                return self.add_data(texts, metadatas, ids)
+            case "delete":
+                return self.delete_data(ids)
+            case _:
+                raise ValueError("Invalid action: {}".format(action))
+
+    def add_data(self, texts, metadatas=None, ids=None):
         # Add the texts to the vector store
-        self.vector_store.add_texts(*args)
+        args = [texts]
+        if metadatas is not None:
+            args.append(metadatas)
+        self.vector_store.add_texts(*args, ids=ids)
+        return {"result": "OK"}
+
+    def delete_data(self, ids):
+        if not ids:
+            raise ValueError("No IDs provided to delete")
+        self.vector_store.delete(ids)
         return {"result": "OK"}

src/solace_ai_connector/components/general/langchain/langchain_vector_store_embedding_search.py

@@ -59,6 +59,7 @@
             "name": "max_results",
             "required": True,
             "description": "The maximum number of results to return",
+            "default": 3,
         },
         {
             "name": "combine_context_from_same_source",
@@ -92,7 +93,7 @@ def __init__(self, **kwargs):
 
     def invoke(self, message, data):
         text = data["text"]
-        k = self.get_config("max_results")
+        k = self.get_config("max_results", 3)
         combine_context_from_same_source = self.get_config(
             "combine_context_from_same_source"
         )