Langchain agent in TypeScript

.gitignore

@@ -432,4 +432,5 @@ FodyWeavers.xsd
 # TypeScript
 dist/
 build/
-node_modules/
+node_modules/
+.tsbuildinfo*

mongo-vcore-agent-langchain/.env.sample

@@ -0,0 +1,79 @@
+DEBUG=false
+USE_PASSWORDLESS=false
+
+# ======================================== 
+# Azure OpenAI Shared Settings 
+# ======================================== 
+#The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.
+AZURE_OPENAI_API_KEY="<your-azure-openai-api-key>" 
+AZURE_OPENAI_ENDPOINT="https://<your-resource-name>.openai.azure.com/" 
+AZURE_OPENAI_API_INSTANCE_NAME="<your-resource-name>" 
+
+AZURE_OPENAI_SYNTH_API_VERSION="2025-01-01-preview"
+AZURE_OPENAI_SYNTH_DEPLOYMENT="gpt-4o"
+
+AZURE_OPENAI_PLANNER_DEPLOYMENT="gpt-4.1-mini"
+AZURE_OPENAI_PLANNER_API_VERSION="2025-01-01-preview"
+
+# ======================================== 
+# Azure OpenAI Embedding Model Settings 
+# ======================================== 
+AZURE_OPENAI_EMBEDDING_DEPLOYMENT="text-embedding-3-small" 
+AZURE_OPENAI_EMBEDDING_API_VERSION="2023-05-15" 
+
+FIELD_TO_EMBED="Description" 
+EMBEDDED_FIELD="vectors" 
+EMBEDDING_DIMENSIONS="1536" 
+EMBEDDING_BATCH_SIZE="16" 
+
+# ======================================== 
+# Data File Paths and Vector Configuration 
+# ======================================== 
+DATA_FILE_WITHOUT_VECTORS="../data/HotelsData_toCosmosDB.JSON" 
+DATA_FILE_WITH_VECTORS="./data/HotelsData_toCosmosDB_Vector_text-embedding-3-small.json" 
+QUERY="quintessential lodging near running trails, eateries, retail"
+# ======================================== 
+# Data Loading and Processing Settings 
+# ======================================== 
+LOAD_SIZE_BATCH="100" 
+# ======================================== 
+# MongoDB/Cosmos DB Connection Settings 
+# ======================================== 
+AZURE_COSMOSDB_MONGODB_CONNECTION_STRING="<your-cosmos-db-connection-string>" 
+MONGO_CLUSTER_NAME="<your-cluster-name>" 
+MONGO_DB_NAME="Hotels" 
+MONGO_DB_COLLECTION="Hotels_ivf"
+MONGO_DB_INDEX_NAME="vectorIndex_ivf"
+# Vector Index Algorithm: vector-ivf (default), vector-hnsw, or vector-diskann
+VECTOR_INDEX_ALGORITHM="vector-ivf"
+VECTOR_SEARCH_STRATEGY="documentdb"  # or mongo or auto
+
+# ======================================== 
+# Vector Index Parameters (Optional Tuning)
+# ======================================== 
+# Common parameter for all algorithms
+VECTOR_SIMILARITY="COS"  # Options: COS (cosine), L2 (euclidean), IP (inner product)
+
+# IVF-specific parameters (used when VECTOR_INDEX_ALGORITHM=vector-ivf)
+IVF_NUM_LISTS="10"  # Number of clusters for IVF index (default: 10)
+
+# HNSW-specific parameters (used when VECTOR_INDEX_ALGORITHM=vector-hnsw)
+# HNSW_M="16"  # Max connections per layer (default: 16)
+# HNSW_EF_CONSTRUCTION="64"  # Size of dynamic candidate list during build (default: 64)
+
+# DiskANN-specific parameters (used when VECTOR_INDEX_ALGORITHM=vector-diskann)
+# DISKANN_MAX_DEGREE="20"  # Max degree of graph (default: 20)
+# DISKANN_L_BUILD="10"  # Size of search list during build (default: 10)
+# ======================================== 
+# Agent Search Configuration 
+# ======================================== 
+MAX_SEARCH_RESULTS="5" 
+SIMILARITY_THRESHOLD="0.7" 
+
+# ======================================== 
+# Optional Settings 
+# ======================================== 
+LANGSMITH_TRACING="false" 
+LANGSMITH_ENDPOINT="https://api.smith.langchain.com" 
+LANGSMITH_API_KEY="" 
+LANGSMITH_PROJECT=""

mongo-vcore-agent-langchain/docs/architecture.mmd

@@ -0,0 +1,21 @@
+```mermaid
+flowchart TD
+  A[User] -->|asks question| Planner(Planner Agent) 
+  Planner -->|calls tool| Tool[getHotelsToMatchSearchQuery Tool]
+  Tool -->|returns plain text / JSON string| Extractor(Extractor)
+  Planner -->|may return AI message instead of tool call| Fallback{Planner didn't call tool}
+  Fallback -->|run programmatic vector search| Extractor
+  Extractor -->|hotelContext string| Synth(Synthesizer Agent)
+  Synth -->|final answer| A
+
+  subgraph Clients
+    E[Embedding Client]
+    C[Cosmos DB (Mongo API) Vector Store]
+    Chat[Chat Client]
+  end
+
+  Tool --> C
+  Tool --> E
+  Planner --> Chat
+  Synth --> Chat
+```