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docs/ai/guide_edgeql.rst

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+.. _ref_ai_guide_edgeql:
+
+=========================
+Guide to Gel AI in EdgeQL
+=========================
+
+:edb-alt-title: How to set up Gel AI in EdgeQL
+
+
+|Gel| AI brings vector search capabilities and retrieval-augmented generation
+directly into the database.
+
+
+Enable and configure the extension
+==================================
+
+.. edb:split-section::
+
+    AI is a |Gel| extension. To enable it, we will need to add the extension
+    to the app’s schema:
+
+    .. code-block:: sdl
+
+        using extension ai;
+
+
+.. edb:split-section::
+
+    |Gel| AI uses external APIs in order to get vectors and LLM completions. For it
+    to work, we need to configure an API provider and specify their API key. Let's
+    open EdgeQL REPL and run the following query:
+
+    .. code-block:: edgeql
+
+        configure current database
+        insert ext::ai::OpenAIProviderConfig {
+          secret := 'sk-....',
+        };
+
+
+Now our |Gel| application can take advantage of OpenAI's API to implement AI
+capabilities.
+
+
+.. note::
+
+   |Gel| AI comes with its own :ref:`UI <ref_ai_extai_reference_ui>` that can
+   be used to configure providers, set up prompts and test them in a sandbox.
+
+
+.. note::
+
+   Most API providers require you to set up and account and charge money for
+   model use.
+
+
+Add vectors and perform similarity search
+=========================================
+
+.. edb:split-section::
+
+    Before we start introducing AI capabilities, let's set up our database with a
+    schema and populate it with some data (we're going to be helping Komi-san keep
+    track of her friends).
+
+    .. code-block:: sdl
+
+        module default {
+            type Friend {
+                required name: str {
+                    constraint exclusive;
+                };
+
+                summary: str;               # A brief description of personality and role
+                relationship_to_komi: str;  # Relationship with Komi
+                defining_trait: str;        # Primary character trait or quirk
+            }
+        }
+
+.. edb:split-section::
+
+    Here's a shell command you can paste and run that will populate the
+    database with some sample data.
+
+    .. code-block:: bash
+        :class: collapsible
+
+        $ cat << 'EOF' > populate_db.edgeql
+        insert Friend {
+            name := 'Tadano Hitohito',
+            summary := 'An extremely average high school boy with a remarkable ability to read the atmosphere and understand others\' feelings, especially Komi\'s.',
+            relationship_to_komi := 'First friend and love interest',
+            defining_trait := 'Perceptiveness',
+        };
+
+        insert Friend {
+            name := 'Osana Najimi',
+            summary := 'An extremely outgoing person who claims to have been everyone\'s childhood friend. Gender: Najimi.',
+            relationship_to_komi := 'Second friend and social catalyst',
+            defining_trait := 'Universal childhood friend',
+        };
+
+        insert Friend {
+            name := 'Yamai Ren',
+            summary := 'An intense and sometimes obsessive classmate who is completely infatuated with Komi.',
+            relationship_to_komi := 'Self-proclaimed guardian and admirer',
+            defining_trait := 'Obsessive devotion',
+        };
+
+        insert Friend {
+            name := 'Katai Makoto',
+            summary := 'A intimidating-looking but shy student who shares many communication problems with Komi.',
+            relationship_to_komi := 'Fellow communication-challenged friend',
+            defining_trait := 'Scary appearance but gentle nature',
+        };
+
+        insert Friend {
+            name := 'Nakanaka Omoharu',
+            summary := 'A self-proclaimed wielder of dark powers who acts like an anime character and is actually just a regular gaming enthusiast.',
+            relationship_to_komi := 'Gaming buddy and chuunibyou friend',
+            defining_trait := 'Chuunibyou tendencies',
+        };
+        EOF
+        $ gel query -f populate_db.edgeql
+
+
+.. edb:split-section::
+
+    In order to get |Gel| to produce embedding vectors, we need to create a special
+    ``deferred index`` on the type we would like to perform similarity search on.
+    More specifically, we need to specify an EdgeQL expression that produces a
+    string that we're going to create an embedding vector for. This is how we would
+    set up an index if we wanted to perform similarity search on
+    ``Friend.summary``:
+
+    .. code-block:: sdl-diff
+
+          module default {
+              type Friend {
+                  required name: str {
+                      constraint exclusive;
+                  };
+
+                  summary: str;               # A brief description of personality and role
+                  relationship_to_komi: str;  # Relationship with Komi
+                  defining_trait: str;        # Primary character trait or quirk
+
+        +         deferred index ext::ai::index(embedding_model := 'text-embedding-3-small')
+        +             on (.summary);
+              }
+          }
+
+
+.. edb:split-section::
+
+    But actually, in our case it would be better if we could similarity search
+    across all properties at the same time. We can define the index on a more
+    complex expression - like a concatenation of string properties - like this:
+
+
+    .. code-block:: sdl-diff
+
+          module default {
+              type Friend {
+                  required name: str {
+                      constraint exclusive;
+                  };
+
+                  summary: str;               # A brief description of personality and role
+                  relationship_to_komi: str;  # Relationship with Komi
+                  defining_trait: str;        # Primary character trait or quirk
+
+                  deferred index ext::ai::index(embedding_model := 'text-embedding-3-small')
+        -             on (.summary);
+        +             on (
+        +                 .name ++ ' ' ++ .summary ++ ' '
+        +                 ++ .relationship_to_komi ++ ' '
+        +                 ++ .defining_trait
+        +             );
+              }
+          }
+
+
+.. edb:split-section::
+
+    Once we're done with schema modification, we need to apply them by going
+    through a migration:
+
+    .. code-block:: bash
+
+        $ gel migration create
+        $ gel migrate
+
+
+.. edb:split-section::
+
+    That's it! |Gel| will make necessary API requests in the background and create an
+    index that will enable us to perform efficient similarity search like this:
+
+    .. code-block:: edgeql
+
+        select ext::ai::search(Friend, query_vector);
+
+
+.. edb:split-section::
+
+    Note that this function accepts an embedding vector as the second argument, not
+    a text string. This means that in order to similarity search for a string, we
+    need to create a vector embedding for it using the same model as we used to
+    create the index. |Gel| offers an HTTP endpoint ``/ai/embeddings`` that can
+    handle it for us. All we need to do is to pass the vector it produces into the
+    search query:
+
+    .. note::
+
+        Note that we're passing our login and password in order to autheticate the
+        request. We can find those using the CLI: ``gel instance credentials
+        --json``. Learn about all the other ways you can authenticate a request
+        :ref:`here <ref_http_auth>`.
+
+    .. code-block:: bash
+
+        $ curl --user user:password \
+          --json '{"input": "Who helps Komi make friends?", "model": "text-embedding-3-small"}' \
+          http://localhost:<port>/branch/main/ai/embeddings \
+          | jq -r '.data[0].embedding' \                                                    # extract the embedding out of the JSON
+          | tr -d '\n' \                                                                    # remove newlines
+          | sed 's/^\[//;s/\]$//' \                                                         # remove square brackets
+          | awk '{print "select ext::ai::search(Friend, <array<float32>>[" $0 "]);"}' \     # assemble the query
+          | gel query --file -  # pass the query into Gel CLI
+
+
+
+Use the built-in RAG
+====================
+
+One more feature |Gel| AI offers is built-in retrieval-augmented generation, also
+known as RAG.
+
+.. edb:split-section::
+
+    |Gel| comes preconfigured to be able to process our text query, perform
+    similarity search across the index we just created, pass the results to an LLM
+    and return a response. We can access the built-in RAG using the ``/ai/rag``
+    HTTP endpoint:
+
+
+    .. code-block:: bash
+
+        $ curl --user user:password --json '{
+            "query": "Who helps Komi make friends?",
+            "model": "gpt-4-turbo-preview",
+            "context": {"query":"select Friend"}
+          }' http://localhost:<port>/branch/main/ai/rag
+
+
+.. edb:split-section::
+
+    We can also stream the response like this:
+
+
+    .. code-block:: bash-diff
+
+          $ curl --user user:password --json '{
+              "query": "Who helps Komi make friends?",
+              "model": "gpt-4-turbo-preview",
+              "context": {"query":"select Friend"},
+        +     "stream": true,
+            }' http://localhost:<port>/branch/main/ai/rag
+
+
+Keep going!
+===========
+
+You are now sufficiently equipped to use |Gel| AI in your applications.
+
+If you'd like to build something on your own, make sure to check out the
+:ref:`Reference manual <ref_ai_extai_reference>` in order to learn the details
+about using different APIs and models, configuring prompts or using the UI.
+Make sure to also check out the |Gel| AI bindings in Python and JavaScript if
+those languages are relevant to you.
+
+And if you would like more guidance for how |Gel| AI can be fit into an
+application, take a look at the FastAPI Gel AI Tutorial, where we're building a
+search bot using features you learned about above.
+