add lightrag dbgpt graph

docs/swagger.yaml

@@ -62,5 +62,196 @@ paths:
                     type: string
                     description: Error message
                     example: "Invalid request body"
+  /idx/milvus:
+    post:
+      summary: Store Milvus index data
+      description: Stores question, answer, and summary data for Milvus indexing.
+      tags:
+        - Indexing
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                question:
+                  type: string
+                  description: The question to store
+                  example: "What is AI?"
+                answer:
+                  type: string
+                  description: The answer to store
+                  example: "AI is artificial intelligence..."
+                summary:
+                  type: string
+                  description: The summary of the Q&A
+                  example: "AI overview"
+                username:
+                  type: string
+                  description: The username of the requester
+                  example: "john_doe"
+                user_id:
+                  type: string
+                  description: The unique identifier for the user
+                  example: "user_12345"
+              required:
+                - question
+                - answer
+      responses:
+        '200':
+          description: Successful storage of Milvus index data
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  id:
+                    type: integer
+                    description: The ID of the stored record
+                    example: 1
+        '400':
+          description: Invalid input
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Invalid request body"
+        '500':
+          description: Server error
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Failed to store data"
+  /idx/graphrag:
+    post:
+      summary: Store GraphRAG index data
+      description: Stores question, answer, and summary data for GraphRAG indexing.
+      tags:
+        - Indexing
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                question:
+                  type: string
+                  description: The question to store
+                  example: "What is NLP?"
+                answer:
+                  type: string
+                  description: The answer to store
+                  example: "NLP is natural language processing..."
+                summary:
+                  type: string
+                  description: The summary of the Q&A
+                  example: "NLP overview"
+                username:
+                  type: string
+                  description: The username of the requester
+                  example: "john_doe"
+                user_id:
+                  type: string
+                  description: The unique identifier for the user
+                  example: "user_12345"
+              required:
+                - question
+                - answer
+      responses:
+        '200':
+          description: Successful storage of GraphRAG index data
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  id:
+                    type: integer
+                    description: The ID of the stored record
+                    example: 1
+        '400':
+          description: Invalid input
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Invalid request body"
+        '500':
+          description: Server error
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Failed to store data"
+  /index:
+    post:
+      summary: Trigger indexing of existing QA data
+      description: Triggers the indexing process for existing QA data in the database using the pgvector extension.
+      tags:
+        - Indexing
+      requestBody:
+        required: false
+        content:
+          application/json:
+            schema:
+              type: object
+              properties:
+                apiKey:
+                  type: string
+                  description: The API key for authentication
+                  example: "sk-1234567890abcdef"
+      responses:
+        '200':
+          description: Indexing process completed successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  message:
+                    type: string
+                    description: Success message
+                    example: "Indexing completed successfully"
+        '400':
+          description: Invalid input
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Invalid API key"
+        '500':
+          description: Server error
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  error:
+                    type: string
+                    description: Error message
+                    example: "Failed to index data"
 components:
   schemas: {}

idx.go

+package main
+
+import (
+	"context"
+	"database/sql"
+	"encoding/json"
+	"fmt"
+	"log"
+	"net/http"
+	"strconv"
+	"time"
+
+	"github.com/firebase/genkit/go/ai"
+	"github.com/firebase/genkit/go/genkit"
+	"github.com/wade-liwei/agentchat/plugins/milvus"
+)
+
+
+func startServer(g *genkit.Genkit, db *sql.DB, indexer ai.Indexer, retriever ai.Retriever, embedder ai.Embedder, apiKey string) {
+    http.HandleFunc("/idx/milvus", handleIndex(indexer, embedder, "Milvus"))
+    http.HandleFunc("/idx/graphrag", handleIndex(indexer, embedder, "GraphRAG")) // 如果需要支持 GraphRAG
+    http.HandleFunc("/index", handleIndexTrigger(g, db, indexer, apiKey))
+    http.HandleFunc("/askQuestion", handleAskQuestion(g, retriever))
+
+    addr := fmt.Sprintf(":%s", *port)
+    log.Printf("Starting server on %s", addr)
+    if err := http.ListenAndServe(addr, nil); err != nil {
+        log.Fatalf("Server failed: %v", err)
+    }
+}
+
+func handleIndex(indexer ai.Indexer, embedder ai.Embedder, indexType string) http.HandlerFunc {
+    return func(w http.ResponseWriter, r *http.Request) {
+        if r.Method != http.MethodPost {
+            http.Error(w, `{"error":"Method not allowed"}`, http.StatusMethodNotAllowed)
+            return
+        }
+
+        var req IndexRequest
+        if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+            http.Error(w, `{"error":"Invalid request body"}`, http.StatusBadRequest)
+            return
+        }
+
+        if req.Question == "" || req.Answer == "" {
+            http.Error(w, `{"error":"Missing required fields: question and answer"}`, http.StatusBadRequest)
+            return
+        }
+
+        var userID *int64
+        if req.UserID != nil {
+            id, err := strconv.ParseInt(*req.UserID, 10, 64)
+            if err != nil {
+                http.Error(w, `{"error":"Invalid user_id format"}`, http.StatusBadRequest)
+                return
+            }
+            userID = &id
+        }
+
+        // 构造文本内容
+        text := req.Question + " " + req.Answer
+        if req.Summary != nil {
+            text += " " + *req.Summary
+        }
+
+        // 构造元数据
+        metadata := map[string]interface{}{
+            "username": req.Username,
+            "user_id":  userID,
+        }
+
+        // 生成唯一 ID（由于 Milvus 插件的 schema 中 idField 是 AutoID，这里仅用于响应）
+        id := time.Now().UnixNano()
+
+        // 创建文档
+        doc := &ai.Document{
+            Content:  []*ai.Part{ai.NewTextPart(text)},
+            Metadata: metadata,
+        }
+
+        // 使用 Indexer 写入 Milvus
+        err := ai.Index(r.Context(), indexer, ai.WithDocs(doc))
+        if err != nil {
+            log.Printf("Failed to index %s data: %v", indexType, err)
+            http.Error(w, `{"error":"Failed to store data"}`, http.StatusInternalServerError)
+            return
+        }
+
+        resp := IndexResponse{ID: id}
+        w.Header().Set("Content-Type", "application/json")
+        w.WriteHeader(http.StatusOK)
+        if err := json.NewEncoder(w).Encode(resp); err != nil {
+            log.Printf("Failed to encode response: %v", err)
+        }
+    }
+}
+
+func handleIndexTrigger(g *genkit.Genkit, db *sql.DB, indexer ai.Indexer, expectedAPIKey string) http.HandlerFunc {
+    return func(w http.ResponseWriter, r *http.Request) {
+        if r.Method != http.MethodPost {
+            http.Error(w, `{"error":"Method not allowed"}`, http.StatusMethodNotAllowed)
+            return
+        }
+
+        var req IndexTriggerRequest
+        if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+            http.Error(w, `{"error":"Invalid request body"}`, http.StatusBadRequest)
+            return
+        }
+
+        if req.APIKey != expectedAPIKey {
+            http.Error(w, `{"error":"Invalid API key"}`, http.StatusBadRequest)
+            return
+        }
+
+        if err := indexExistingRows(r.Context(), db, indexer); err != nil {
+            log.Printf("Failed to index data: %v", err)
+            http.Error(w, `{"error":"Failed to index data"}`, http.StatusInternalServerError)
+            return
+        }
+
+        resp := IndexTriggerResponse{Message: "Indexing completed successfully"}
+        w.Header().Set("Content-Type", "application/json")
+        w.WriteHeader(http.StatusOK)
+        if err := json.NewEncoder(w).Encode(resp); err != nil {
+            log.Printf("Failed to encode response: %v", err)
+        }
+    }
+}
+
+
+func handleAskQuestion(g *genkit.Genkit, retriever ai.Retriever) http.HandlerFunc {
+    return func(w http.ResponseWriter, r *http.Request) {
+        if r.Method != http.MethodPost {
+            http.Error(w, `{"error":"Method not allowed"}`, http.StatusMethodNotAllowed)
+            return
+        }
+
+        var input struct {
+            Question string `json:"Question"`
+            Show     string `json:"Show"`
+        }
+        if err := json.NewDecoder(r.Body).Decode(&input); err != nil {
+            http.Error(w, `{"error":"Invalid request body"}`, http.StatusBadRequest)
+            return
+        }
+
+        if input.Question == "" || input.Show == "" {
+            http.Error(w, `{"error":"Missing required fields: Question and Show"}`, http.StatusBadRequest)
+            return
+        }
+
+        // 创建查询文档
+        queryDoc := &ai.Document{
+            Content: []*ai.Part{ai.NewTextPart(input.Question)},
+        }
+
+        // 使用 Retriever 检索
+        retrieverOptions := &milvus.RetrieverOptions{
+            Count:      3, // 获取前 3 个结果
+            MetricType: "L2",
+        }
+        result, err := ai.Retrieve(r.Context(), retriever, ai.WithQuery(queryDoc), ai.WithOptions(retrieverOptions))
+        if err != nil {
+            log.Printf("Failed to retrieve data: %v", err)
+            http.Error(w, `{"error":"Failed to process question"}`, http.StatusInternalServerError)
+            return
+        }
+
+        // 构造响应（可以根据需要处理检索结果）
+        var responseText string
+        for _, doc := range result.Documents {
+            for _, part := range doc.Content {
+                if part.IsText() {
+                    responseText += part.Text + "\n"
+                }
+            }
+        }
+
+        resp := struct {
+            Response string `json:"response"`
+        }{Response: responseText}
+        w.Header().Set("Content-Type", "application/json")
+        w.WriteHeader(http.StatusOK)
+        if err := json.NewEncoder(w).Encode(resp); err != nil {
+            log.Printf("Failed to encode response: %v", err)
+        }
+    }
+}
+
+func indexExistingRows(ctx context.Context, db *sql.DB, indexer ai.Indexer) error {
+    rows, err := db.QueryContext(ctx, `SELECT id, question, answer, summary FROM qa`)
+    if err != nil {
+        return err
+    }
+    defer rows.Close()
+
+    var docs []*ai.Document
+    for rows.Next() {
+        var id int64
+        var question, answer, summary sql.NullString
+        if err := rows.Scan(&id, &question, &answer, &summary); err != nil {
+            return err
+        }
+        content := question.String
+        if answer.Valid {
+            content += " " + answer.String
+        }
+        if summary.Valid {
+            content += " " + summary.String
+        }
+        docs = append(docs, &ai.Document{
+            Content: []*ai.Part{ai.NewTextPart(content)},
+            Metadata: map[string]interface{}{
+                "id": id,
+            },
+        })
+    }
+    if err := rows.Err(); err != nil {
+        return err
+    }
+    return ai.Index(ctx, indexer, ai.WithDocs(docs...))
+}
\ No newline at end of file

plugins/graphrag/dbgpt-graphrag.toml

+ubuntu@ip-172-26-8-199:~/DB-GPT/configs$ cat  dbgpt-graphrag.toml 
+[system]
+# Load language from environment variable(It is set by the hook)
+language = "${env:DBGPT_LANG:-zh}"
+log_level = "INFO"
+api_keys = []
+encrypt_key = "your_secret_key"
+
+# Server Configurations
+[service.web]
+host = "0.0.0.0"
+port = 5670
+
+#[service.web.database]
+#type = "sqlite"
+#path = "pilot/meta_data/dbgpt.db"
+
+[service.web.database]
+type = "mysql"
+host = "${env:MYSQL_HOST:-127.0.0.1}"
+port = "${env:MYSQL_PORT:-3306}"
+database = "${env:MYSQL_DATABASE:-dbgpt}"
+user = "${env:MYSQL_USER:-root}"
+password ="${env:MYSQL_PASSWORD:-aa123456}"
+
+
+[rag]
+chunk_size=1000
+chunk_overlap=0
+similarity_top_k=5
+similarity_score_threshold=0.0
+max_chunks_once_load=10
+max_threads=1
+rerank_top_k=3
+
+[rag.storage]
+[rag.storage.vector]
+type = "chroma"
+persist_path = "pilot/data"
+
+[rag.storage.graph]
+type = "tugraph"
+host="tugraph"
+port=7687
+username="admin"
+password="73@TuGraph"
+
+# enable_summary="True"
+# community_topk=20
+# community_score_threshold=0.3
+
+# triplet_graph_enabled="True"
+# extract_topk=20
+
+# document_graph_enabled="True"
+# knowledge_graph_chunk_search_top_size=20
+# knowledge_graph_extraction_batch_size=20
+
+# enable_similarity_search="True"
+# knowledge_graph_embedding_batch_size=20
+# similarity_search_topk=5
+# extract_score_threshold=0.7
+
+# enable_text_search="True"
+# text2gql_model_enabled="True"
+# text2gql_model_name="qwen2.5:latest"
+
+
+
+# Model Configurations
+[models]
+[[models.llms]]
+name = "Qwen/Qwen2.5-Coder-32B-Instruct"
+provider = "proxy/siliconflow"
+api_key = "${env:SILICONFLOW_API_KEY}"
+
+[[models.embeddings]]
+name = "BAAI/bge-m3"
+provider = "proxy/siliconflow"
+api_key = "${env:SILICONFLOW_API_KEY}"
+
+
+#[models]
+#[[models.llms]]
+#name = "${env:LLM_MODEL_NAME:-gpt-4o}"
+#provider = "${env:LLM_MODEL_PROVIDER:-proxy/openai}"
+#api_base = "${env:OPENAI_API_BASE:-https://api.openai.com/v1}"
+#api_base = "${env:OPENAI_API_BASE:-https://aihubmix.com/v1}"
+#api_key = "${env:OPENAI_API_KEY}"
+
+#[[models.embeddings]]
+#name = "${env:EMBEDDING_MODEL_NAME:-text-embedding-3-small}"
+#provider = "${env:EMBEDDING_MODEL_PROVIDER:-proxy/openai}"
+#api_url = "${env:EMBEDDING_MODEL_API_URL:-https://aihubmix.com/v1/embeddings}"
+#api_key = "${env:OPENAI_API_KEY}"
+ubuntu@ip-172-26-8-199:~/DB-GPT/configs$ 

plugins/graphrag/docker-compose.yml

+ubuntu@ip-172-26-8-199:~/DB-GPT$ cat  docker-compose.yml
+
+# To run current docker compose file, you should prepare the siliconflow api key in your environment.
+# SILICONFLOW_API_KEY=${SILICONFLOW_API_KEY} docker compose up -d
+services:
+  db:
+    image: mysql/mysql-server
+    environment:
+      MYSQL_USER: 'user'
+      MYSQL_PASSWORD: 'password'
+      MYSQL_ROOT_PASSWORD: 'aa123456'
+    ports:
+      - 3306:3306
+    volumes:
+      - dbgpt-myql-db:/var/lib/mysql
+      - ./docker/examples/my.cnf:/etc/my.cnf
+      - ./docker/examples/sqls:/docker-entrypoint-initdb.d
+      - ./assets/schema/dbgpt.sql:/docker-entrypoint-initdb.d/dbgpt.sql
+    restart: unless-stopped
+    networks:
+      - dbgptnet
+  webserver:
+    image: eosphorosai/dbgpt-openai:latest
+    command: dbgpt start webserver --config /app/configs/dbgpt-graphrag.toml
+    environment:
+      - SILICONFLOW_API_KEY=${SILICONFLOW_API_KEY}
+      - MYSQL_PASSWORD=aa123456
+      - MYSQL_HOST=db
+      - MYSQL_PORT=3306
+      - MYSQL_DATABASE=dbgpt
+      - MYSQL_USER=root
+      - OPENAI_API_KEY=sk-UIpD9DohtE0Ok4wtFdC21668Dc3241629e8aA05d5dAeFdA1
+    volumes:
+      - ./configs:/app/configs
+      - /data:/data
+      # May be you can mount your models to container
+      - /data/models:/app/models
+      - dbgpt-data:/app/pilot/data
+      - dbgpt-message:/app/pilot/message
+    depends_on:
+      - db
+      - tugraph
+    ports:
+      - 5670:5670/tcp
+    # webserver may be failed, it must wait all sqls in /docker-entrypoint-initdb.d execute finish.
+    restart: unless-stopped
+    networks:
+      - dbgptnet
+    ipc: host
+  tugraph:
+    image: tugraph/tugraph-runtime-centos7:4.5.1
+    command: lgraph_server -d run --enable_plugin true
+    ports:
+      - 7070:7070
+      - 7687:7687
+      - 9090:9090
+    container_name: tugraph_demo
+    restart: unless-stopped
+    networks:
+      - dbgptnet
+volumes:
+  dbgpt-myql-db:
+  dbgpt-data:
+  dbgpt-message:
+  dbgpt-alembic-versions:
+networks:
+  dbgptnet:
+    driver: bridge
+    name: dbgptnet
+
+
+ubuntu@ip-172-26-8-199:~/DB-GPT$ 

plugins/lightrag/docker-compose.yml

+ubuntu@ip-172-26-8-199:~/LightRAG$ cat docker-compose.yml
+services:
+  pgvector:
+    image: pgvector/pgvector:pg16
+    profiles:
+      - pgvector
+    restart: always
+    environment:
+      PGUSER: ${PGVECTOR_PGUSER:-postgres}
+      # The password for the default postgres user.
+      POSTGRES_PASSWORD: ${PGVECTOR_POSTGRES_PASSWORD:-lightrag123456}
+      # The name of the default postgres database.
+      POSTGRES_DB: ${PGVECTOR_POSTGRES_DB:-lightrag}
+      # postgres data directory
+      PGDATA: ${PGVECTOR_PGDATA:-/var/lib/postgresql/data/pgdata}
+      # pg_bigm module for full text search
+      PG_BIGM: ${PGVECTOR_PG_BIGM:-false}
+      PG_BIGM_VERSION: ${PGVECTOR_PG_BIGM_VERSION:-1.2-20240606}
+    volumes:
+      - ./volumes/pgvector/data:/var/lib/postgresql/data
+      - ./pgvector/docker-entrypoint.sh:/docker-entrypoint.sh
+    entrypoint: [ '/docker-entrypoint.sh' ]
+    networks:
+      - lightrag-net
+    healthcheck:
+      test: [ 'CMD', 'pg_isready' ]
+      interval: 1s
+      timeout: 3s
+      retries: 30
+  neo4j:
+    image: neo4j:latest
+    volumes:
+        - ./neo4j/logs:/logs
+        - ./neo4j/config:/config
+        - ./neo4j/data:/data
+        - ./neo4j/plugins:/plugins
+    environment:
+        - NEO4J_AUTH=neo4j/mysecretpassword123 # This sets username and password
+    ports:
+      - "7474:7474" # Neo4j Browser HTTP
+      - "7687:7687" # Bolt protocol for applications
+    restart: always
+    networks:
+      - lightrag-net
+  lightrag:
+    container_name: lightrag
+    image: ghcr.io/hkuds/lightrag:latest
+    build:
+      context: .
+      dockerfile: Dockerfile
+      tags:
+        - ghcr.io/hkuds/lightrag:latest
+    ports:
+      - "${PORT:-9621}:9621"
+    volumes:
+      - ./data/rag_storage:/app/data/rag_storage
+      - ./data/inputs:/app/data/inputs
+      - ./config.ini:/app/config.ini
+      - ./.env:/app/.env
+    env_file:
+      - .env
+    restart: unless-stopped
+    networks:
+      - lightrag-net
+    depends_on:  # <--- 在这里增加依赖
+      - neo4j
+
+networks:
+  lightrag-net:  
+
+ubuntu@ip-172-26-8-199:~/LightRAG$ 

plugins/lightrag/env

+ubuntu@ip-172-26-8-199:~/LightRAG$ cat .env 
+### This is sample file of .env
+
+
+### Server Configuration
+HOST=0.0.0.0
+PORT=9621
+WEBUI_TITLE='My Graph KB'
+WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
+OLLAMA_EMULATING_MODEL_TAG=latest
+# WORKERS=2
+# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
+
+### Login Configuration
+# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
+# TOKEN_EXPIRE_HOURS=48
+# GUEST_TOKEN_EXPIRE_HOURS=24
+# JWT_ALGORITHM=HS256
+
+### API-Key to access LightRAG Server API
+# LIGHTRAG_API_KEY=your-secure-api-key-here
+# WHITELIST_PATHS=/health,/api/*
+
+### Optional SSL Configuration
+# SSL=true
+# SSL_CERTFILE=/path/to/cert.pem
+# SSL_KEYFILE=/path/to/key.pem
+
+### Directory Configuration (defaults to current working directory)
+### Should not be set if deploy by docker (Set by Dockerfile instead of .env)
+### Default value is ./inputs and ./rag_storage
+# INPUT_DIR=<absolute_path_for_doc_input_dir>
+# WORKING_DIR=<absolute_path_for_working_dir>
+
+### Max nodes return from grap retrieval
+# MAX_GRAPH_NODES=1000
+
+### Logging level
+# LOG_LEVEL=INFO
+# VERBOSE=False
+# LOG_MAX_BYTES=10485760
+# LOG_BACKUP_COUNT=5
+### Logfile location (defaults to current working directory)
+# LOG_DIR=/path/to/log/directory
+
+### Settings for RAG query
+# HISTORY_TURNS=3
+# COSINE_THRESHOLD=0.2
+# TOP_K=60
+# MAX_TOKEN_TEXT_CHUNK=4000
+# MAX_TOKEN_RELATION_DESC=4000
+# MAX_TOKEN_ENTITY_DESC=4000
+
+### Entity and ralation summarization configuration
+### Language: English, Chinese, French, German ...
+SUMMARY_LANGUAGE=English
+### Number of duplicated entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented)
+# FORCE_LLM_SUMMARY_ON_MERGE=6
+### Max tokens for entity/relations description after merge
+# MAX_TOKEN_SUMMARY=500
+
+### Number of parallel processing documents(Less than MAX_ASYNC/2 is recommended)
+# MAX_PARALLEL_INSERT=2
+### Chunk size for document splitting, 500~1500 is recommended
+# CHUNK_SIZE=1200
+# CHUNK_OVERLAP_SIZE=100
+
+### LLM Configuration
+ENABLE_LLM_CACHE=true
+ENABLE_LLM_CACHE_FOR_EXTRACT=true
+### Time out in seconds for LLM, None for infinite timeout
+TIMEOUT=240
+### Some models like o1-mini require temperature to be set to 1
+TEMPERATURE=0
+### Max concurrency requests of LLM
+MAX_ASYNC=4
+### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model)
+### MAX_TOKENS: set as num_ctx option for Ollama by API Server
+MAX_TOKENS=32768
+### LLM Binding type: openai, ollama, lollms, azure_openai
+#LLM_BINDING=openai
+#LLM_MODEL=gpt-4o
+#LLM_BINDING_HOST=https://api.openai.com/v1
+#LLM_BINDING_API_KEY=your_api_key
+### Optional for Azure
+# AZURE_OPENAI_API_VERSION=2024-08-01-preview
+# AZURE_OPENAI_DEPLOYMENT=gpt-4o
+
+### Embedding Configuration
+### Embedding Binding type: openai, ollama, lollms, azure_openai
+#EMBEDDING_BINDING=ollama
+#EMBEDDING_MODEL=bge-m3:latest
+#EMBEDDING_DIM=1024
+#EMBEDDING_BINDING_API_KEY=your_api_key
+# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
+#EMBEDDING_BINDING_HOST=http://localhost:11434
+
+
+
+### OpenAI alike example
+LLM_BINDING=openai
+LLM_MODEL=deepseek-chat
+LLM_BINDING_HOST=https://api.deepseek.com
+LLM_BINDING_API_KEY=sk-9f70df871a7c4b8aa566a3c7a0603706
+
+
+### Optional for Azure
+# AZURE_OPENAI_API_VERSION=2024-08-01-preview
+# AZURE_OPENAI_DEPLOYMENT=gpt-4o
+
+### Embedding Configuration
+### Embedding Binding type: openai, ollama, lollms, azure_openai
+
+#EMBEDDING_BINDING=ollama
+#EMBEDDING_MODEL=bge-m3:latest
+#EMBEDDING_DIM=1024
+#EMBEDDING_BINDING_API_KEY=your_api_key
+# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
+#EMBEDDING_BINDING_HOST=http://localhost:11434
+
+# EMBEDDING_BINDING=ollama
+
+EMBEDDING_BINDING=openai  # Change from 'ollama' to 'openai'
+EMBEDDING_MODEL=BAAI/bge-large-zh-v1.5
+EMBEDDING_DIM=1024
+EMBEDDING_BINDING_API_KEY=sk-ogigzbogipwhtkwvwnoeiovjdalkotopnpkwkxlvsvjsmyms
+EMBEDDING_BINDING_HOST=https://api.siliconflow.cn/v1
+
+
+
+
+
+
+### Num of chunks send to Embedding in single request
+# EMBEDDING_BATCH_NUM=32
+### Max concurrency requests for Embedding
+# EMBEDDING_FUNC_MAX_ASYNC=16
+### Maximum tokens sent to Embedding for each chunk (no longer in use?)
+# MAX_EMBED_TOKENS=8192
+### Optional for Azure
+# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
+# AZURE_EMBEDDING_API_VERSION=2023-05-15
+
+### Data storage selection
+# LIGHTRAG_KV_STORAGE=PGKVStorage
+# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
+# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
+# LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
+
+### TiDB Configuration (Deprecated)
+# TIDB_HOST=localhost
+# TIDB_PORT=4000
+# TIDB_USER=your_username
+# TIDB_PASSWORD='your_password'
+# TIDB_DATABASE=your_database
+### separating all data from difference Lightrag instances(deprecating)
+# TIDB_WORKSPACE=default
+
+### PostgreSQL Configuration
+POSTGRES_HOST=localhost
+POSTGRES_PORT=5432
+POSTGRES_USER=your_username
+POSTGRES_PASSWORD='your_password'
+POSTGRES_DATABASE=your_database
+POSTGRES_MAX_CONNECTIONS=12
+### separating all data from difference Lightrag instances(deprecating)
+# POSTGRES_WORKSPACE=default
+
+### Neo4j Configuration
+NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
+NEO4J_USERNAME=neo4j
+NEO4J_PASSWORD='your_password'
+
+### Independent AGM Configuration(not for AMG embedded in PostreSQL)
+# AGE_POSTGRES_DB=
+# AGE_POSTGRES_USER=
+# AGE_POSTGRES_PASSWORD=
+# AGE_POSTGRES_HOST=
+# AGE_POSTGRES_PORT=8529
+
+# AGE Graph Name(apply to PostgreSQL and independent AGM)
+### AGE_GRAPH_NAME is precated
+# AGE_GRAPH_NAME=lightrag
+
+### MongoDB Configuration
+MONGO_URI=mongodb://root:root@localhost:27017/
+MONGO_DATABASE=LightRAG
+### separating all data from difference Lightrag instances(deprecating)
+# MONGODB_GRAPH=false
+
+### Milvus Configuration
+MILVUS_URI=http://localhost:19530
+MILVUS_DB_NAME=lightrag
+# MILVUS_USER=root
+# MILVUS_PASSWORD=your_password
+# MILVUS_TOKEN=your_token
+
+### Qdrant
+QDRANT_URL=http://localhost:16333
+# QDRANT_API_KEY=your-api-key
+
+### Redis
+REDIS_URI=redis://localhost:6379
+ubuntu@ip-172-26-8-199:~/LightRAG$