v2.3.43

cookbook/gemini/README.md

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+# Gemini Cookbook
+
+> Note: Fork and clone this repository if needed
+
+## Prerequisites
+
+1. [Install](https://cloud.google.com/sdk/docs/install) the Google Cloud SDK
+2. [Create a Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects)
+3. [Enable the AI Platform API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)
+4. [Authenticate](https://cloud.google.com/sdk/docs/initializing) with Google Cloud
+
+```shell
+gcloud auth application-default login
+```
+
+## Build Assistants using Gemini
+
+1. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+2. Install libraries
+
+```shell
+pip install -U google-cloud-aiplatform phidata
+```
+
+3. Export the following environment variables
+
+```shell
+export PROJECT_ID=your-project-id
+export LOCATION=us-central1
+```
+
+4. Run Assistant
+
+```shell
+python cookbook/gemini/assistant.py
+```
+
+5. Run Assistant with Tool calls
+
+```shell
+pip install duckduckgo-search
+
+python cookbook/gemini/tool_call.py
+```
+
+## Build RAG AI App using Gemini + PgVector
+
+1. Start pgvector
+
+```shell
+phi start cookbook/gemini/resources.py -y
+```
+
+2. Install libraries
+
+```shell
+pip install -U pgvector pypdf psycopg sqlalchemy google-cloud-aiplatform phidata
+```
+
+3. Run RAG App
+
+```shell
+python cookbook/gemini/app.py
+```
+
+4. Stop pgvector
+
+```shell
+phi stop cookbook/gemini/resources.py -y
+```

cookbook/gemini/assistant.py

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+from os import getenv
+
+import vertexai
+from phi.assistant import Assistant
+from phi.llm.gemini import Gemini
+
+# *********** Initialize VertexAI ***********
+vertexai.init(project=getenv("PROJECT_ID"), location=getenv("LOCATION"))
+
+assistant = Assistant(
+    llm=Gemini(model="gemini-1.0-pro-vision"),
+    description="You help people with their health and fitness goals.",
+)
+assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True)

cookbook/gemini/data_analyst.py

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+import json
+from textwrap import dedent
+from os import getenv
+
+import vertexai
+from phi.assistant import Assistant
+from phi.tools.duckdb import DuckDbTools
+from phi.llm.gemini import Gemini
+
+# *********** Initialize VertexAI ***********
+vertexai.init(project=getenv("PROJECT_ID"), location=getenv("LOCATION"))
+
+duckdb_assistant = Assistant(
+    llm=Gemini(model="gemini-pro"),
+    tools=[DuckDbTools()],
+    description="You are an expert data engineer that writes DuckDb queries to analyze data.",
+    instructions=[
+        "Using the `semantic_model` below, find which tables and columns you need to accomplish the task.",
+        "If you need to run a query, run `show_tables` to check the tables you need exist.",
+        "If the tables do not exist, RUN `create_table_from_path` to create the table using the path from the `semantic_model`",
+        "Once you have the tables and columns, create one single syntactically correct DuckDB query.",
+        "If you need to join tables, check the `semantic_model` for the relationships between the tables.",
+        "If the `semantic_model` contains a relationship between tables, use that relationship to join the tables even if the column names are different.",
+        "Inspect the query using `inspect_query` to confirm it is correct.",
+        "If the query is valid, RUN the query using the `run_query` function",
+        "Analyse the results and return the answer to the user.",
+        "Continue till you have accomplished the task.",
+        "Show the user the SQL you ran",
+    ],
+    add_to_system_prompt=dedent(
+        """
+    You have access to the following semantic_model:
+    <semantic_model>
+    {}
+    </semantic_model>
+    """
+    ).format(
+        json.dumps(
+            {
+                "tables": [
+                    {
+                        "name": "movies",
+                        "description": "Contains information about movies from IMDB.",
+                        "path": "https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
+                    }
+                ]
+            }
+        )
+    ),
+    show_tool_calls=True,
+    debug_mode=True,
+)
+
+duckdb_assistant.print_response("What is the average rating of movies? Show me the SQL.", markdown=True)

cookbook/gemini/resources.py

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+from phi.docker.app.postgres import PgVectorDb
+from phi.docker.resources import DockerResources
+
+# -*- PgVector running on port 5432:5432
+vector_db = PgVectorDb(
+    pg_user="ai",
+    pg_password="ai",
+    pg_database="ai",
+)
+
+# -*- DockerResources
+dev_docker_resources = DockerResources(apps=[vector_db])

cookbook/gemini/samples/README.md

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+# Gemini Code Samples
+
+This directory contains code samples for directly querying the Gemini API.
+While these code samples don't use phidata, they are intended to help you test and get started with the Gemini API.
+
+## Prerequisites
+
+1. [Install](https://cloud.google.com/sdk/docs/install) the Google Cloud SDK
+2. [Create a Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects)
+3. [Enable the AI Platform API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)
+4. [Authenticate](https://cloud.google.com/sdk/docs/initializing) with Google Cloud
+
+```shell
+gcloud auth application-default login
+```
+
+5. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+6. Install `google-cloud-aiplatform` library
+
+```shell
+pip install -U google-cloud-aiplatform
+```
+
+7. Export the following environment variables
+
+```shell
+export PROJECT_ID=your-project-id
+export LOCATION=us-central1
+```
+
+## Run the code samples
+
+1. Multimodal example
+
+```shell
+python cookbook/gemini/samples/multimodal.py
+```

cookbook/gemini/samples/multimodal.py

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+from os import getenv
+from typing import Optional
+
+import vertexai
+from vertexai.generative_models import GenerativeModel, Part
+
+
+def multimodal_example(project: Optional[str], location: Optional[str]) -> str:
+    # Initialize Vertex AI
+    vertexai.init(project=project, location=location)
+    # Load the model
+    multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
+    # Query the model
+    response = multimodal_model.generate_content(
+        [
+            # Add an example image
+            Part.from_uri("gs://generativeai-downloads/images/scones.jpg", mime_type="image/jpeg"),
+            # Add an example query
+            "what is shown in this image?",
+        ]
+    )
+    print("============= RESPONSE =============")
+    print(response)
+    print("============= RESPONSE =============")
+    return response.text
+
+
+# *********** Get project and location ***********
+PROJECT_ID = getenv("PROJECT_ID")
+LOCATION = getenv("LOCATION")
+
+# *********** Run the example ***********
+if __name__ == "__main__":
+    result = multimodal_example(project=PROJECT_ID, location=LOCATION)
+    print("============= RESULT =============")
+    print(result)
+    print("============= RESULT =============")

cookbook/gemini/samples/text_stream.py

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+from os import getenv
+from typing import Iterable, Optional
+
+import vertexai
+from vertexai.generative_models import GenerativeModel, GenerationResponse
+
+
+def generate(project: Optional[str], location: Optional[str]) -> None:
+    # Initialize Vertex AI
+    vertexai.init(project=project, location=location)
+    # Load the model
+    model = GenerativeModel("gemini-1.0-pro-vision")
+    # Query the model
+    responses: Iterable[GenerationResponse] = model.generate_content("Who are you?", stream=True)
+    # Process the response
+    for response in responses:
+        print(response.text, end="")
+    print(" ")
+
+
+# *********** Get project and location ***********
+PROJECT_ID = getenv("PROJECT_ID")
+LOCATION = getenv("LOCATION")
+
+# *********** Run the example ***********
+if __name__ == "__main__":
+    generate(project=PROJECT_ID, location=LOCATION)

cookbook/gemini/tool_call.py

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+from os import getenv
+
+import vertexai
+from phi.assistant import Assistant
+from phi.llm.gemini import Gemini
+from phi.tools.duckduckgo import DuckDuckGo
+
+# *********** Initialize VertexAI ***********
+vertexai.init(project=getenv("PROJECT_ID"), location=getenv("LOCATION"))
+
+assistant = Assistant(
+    llm=Gemini(model="gemini-pro"),
+    tools=[DuckDuckGo()],
+    show_tool_calls=True,
+)
+assistant.print_response("Whats happening in France?  Summarize top 10 stories with sources", markdown=True)

cookbook/mistral/README.md

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+# Mistral AI
+
+> Note: Fork and clone this repository if needed
+
+## RAG AI App with Mistral & PgVector
+
+1. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+2. Export your Mistral API Key
+
+```shell
+export MISTRAL_API_KEY=xxx
+```
+
+3. Start pgvector
+
+```shell
+phi start cookbook/mistral/resources.py -y
+```
+
+4. Install libraries
+
+```shell
+pip install -r cookbook/mistral/requirements.txt
+```
+
+5. Run RAG App
+
+```shell
+streamlit run cookbook/mistral/app.py
+```
+
+6. Stop pgvector
+
+```shell
+phi stop cookbook/mistral/resources.py -y
+```
+
+## Build AI Assistants with Mistral
+
+1. Install libraries
+
+```shell
+pip install -U mistralai phidata
+```
+
+2. Run Assistant
+
+```shell
+python cookbook/mistral/simple_assistant.py
+```
+
+3. Output Pydantic models
+
+```shell
+python cookbook/mistral/pydantic_output.py
+```
+
+4. Run Assistant with Tool calls
+
+> NOTE: currently not working
+
+```shell
+pip install duckduckgo-search
+
+python cookbook/mistral/tool_call.py
+```
+
+Optional: View Mistral models
+
+```shell
+python cookbook/mistral/list_models.py
+```