Add full YAML at vectorDB integration docs

docs/source/integration/vectordb/couchbase.md

@@ -38,7 +38,7 @@ This should correspond to the `dimension` of the embeddings generated by the spe
 ### Example YAML file
 
 ```yaml
-- name: openai_embed_3_large
+- name: openai_couchbase
   db_type: couchbase
   embedding_model: openai_embed_3_large
   bucket_name: autorag   # replace your bucket name
@@ -50,6 +50,47 @@ This should correspond to the `dimension` of the embeddings generated by the spe
   password: ${COUCHBASE_PASSWORD"}
 ```
 
+Here is a simple example of a YAML configuration file that uses the Couchbase vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_couchbase
+    db_type: couchbase
+    embedding_model: openai_embed_3_large
+    bucket_name: autorag   # replace your bucket name
+    scope_name: autorag   # replace your scope name
+    collection_name: autorag   # replace your collection name
+    index_name: autorag_search   # replace your index name
+    connection_string: ${COUCHBASE_CONNECTION_STRING}
+    username: ${COUCHBASE_USERNAME"}
+    password: ${COUCHBASE_PASSWORD"}
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_couchbase
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 ### Parameters
 
 1. `embedding_model: str`

docs/source/integration/vectordb/milvus.md

@@ -7,7 +7,7 @@ The `Milvus` class is a vector database implementation that allows you to store,
 To use the Milvus vector database, you need to configure it in your YAML configuration file. Here's an example configuration:
 
 ```yaml
-- name: openai_embed_3_large
+- name: openai_milvus
   db_type: milvus
   embedding_model: openai_embed_3_large
   collection_name: openai_embed_3_large
@@ -17,6 +17,45 @@ To use the Milvus vector database, you need to configure it in your YAML configu
   similarity_metric: cosine
 ```
 
+Here is a simple example of a YAML configuration file that uses the Milvus vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_milvus
+    db_type: milvus
+    embedding_model: openai_embed_3_large
+    collection_name: openai_embed_3_large
+    uri: ${MILVUS_URI}
+    token: ${MILVUS_TOKEN}
+    embedding_batch: 50
+    similarity_metric: cosine
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_milvus
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 1. `embedding_model: str`
    - Purpose: Specifies the name or identifier of the embedding model to be used.
    - Example: "openai_embed_3_large"

docs/source/integration/vectordb/pinecone.md

@@ -12,7 +12,7 @@ You can get the API key from [here](https://app.pinecone.io/organizations/-/keys
 ### Example YAML file
 
 ```yaml
-- name: openai_embed_3_large
+- name: openai_pinecone
   db_type: pinecone
   embedding_model: openai_embed_3_large
   index_name: openai_embed_3_large
@@ -21,6 +21,44 @@ You can get the API key from [here](https://app.pinecone.io/organizations/-/keys
   dimension: 1536
 ```
 
+Here is a simple example of a YAML configuration file that uses the Pinecone vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_pinecone
+    db_type: pinecone
+    embedding_model: openai_embed_3_large
+    index_name: openai_embed_3_large
+    api_key: ${PINECONE_API_KEY}
+    similarity_metric: cosine
+    dimension: 1536
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_pinecone
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 ### Parameters
 
 1. `embedding_model: str`

docs/source/integration/vectordb/qdrant.md

@@ -11,7 +11,7 @@ Its capabilities are particularly beneficial for developing applications that re
 To use the Qdrant vector database, you need to configure it in your YAML configuration file. Here's an example configuration:
 
 ```yaml
-- name: openai_embed_3_large
+- name: openai_qdrant
   db_type: qdrant
   embedding_model: openai_embed_3_large
   collection_name: openai_embed_3_large
@@ -21,6 +21,45 @@ To use the Qdrant vector database, you need to configure it in your YAML configu
   dimension: 1536
 ```
 
+Here is a simple example of a YAML configuration file that uses the Qdrant vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_qdrant
+    db_type: qdrant
+    embedding_model: openai_embed_3_large
+    collection_name: openai_embed_3_large
+    client_type: docker
+    embedding_batch: 50
+    similarity_metric: cosine
+    dimension: 1536
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_qdrant
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 1. `embedding_model: str`
    - Purpose: Specifies the name or identifier of the embedding model to be used.
    - Example: "openai_embed_3_large"

docs/source/integration/vectordb/weaviate.md

@@ -24,7 +24,7 @@ If you already have content stored in weaviate, you'll need to set that key valu
 #### Example YAML file
 
 ```yaml
-- name: openai_embed_3_large
+- name: openai_weaviate
   db_type: weaviate
   embedding_model: openai_embed_3_large
   collection_name: openai_embed_3_large
@@ -37,6 +37,48 @@ If you already have content stored in weaviate, you'll need to set that key valu
   text_key: content
 ```
 
+Here is a simple example of a YAML configuration file that uses the Weaviate vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_weaviate
+    db_type: weaviate
+    embedding_model: openai_embed_3_large
+    collection_name: openai_embed_3_large
+    client_type: docker
+    host: localhost
+    port: 8080
+    grpc_port: 50051
+    embedding_batch: 50
+    similarity_metric: cosine
+    text_key: content
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_weaviate
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 ### 2. Weaviate Cloud
 
 You can see the full installation guide [here](https://weaviate.io/developers/weaviate/installation/weaviate-cloud-services)
@@ -56,6 +98,47 @@ You can see the full installation guide [here](https://weaviate.io/developers/we
   text_key: content
 ```
 
+Here is a simple example of a YAML configuration file that uses the Weaviate vector database and the OpenAI:
+
+```yaml
+vectordb:
+  - name: openai_weaviate
+    db_type: weaviate
+    embedding_model: openai_embed_3_large
+    collection_name: openai_embed_3_large
+    url: ${WEAVIATE_URL}
+    api_key: ${WEAVIATE_API_KEY}
+    grpc_port: 50051
+    embedding_batch: 50
+    similarity_metric: cosine
+    text_key: content
+node_lines:
+- node_line_name: retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: retrieval
+      strategy:
+        metrics: [retrieval_f1, retrieval_recall, retrieval_precision]
+      top_k: 3
+      modules:
+        - module_type: vectordb
+          vectordb: openai_weaviate
+- node_line_name: post_retrieve_node_line  # Arbitrary node line name
+  nodes:
+    - node_type: prompt_maker
+      strategy:
+        metrics: [bleu, meteor, rouge]
+      modules:
+        - module_type: fstring
+          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
+    - node_type: generator
+      strategy:
+        metrics: [bleu, rouge]
+      modules:
+        - module_type: llama_index_llm
+          llm: openai
+          model: [ gpt-4o-mini ]
+```
+
 ### Parameters
 
 1. `embedding_model: str`